IRT -- Problem (?) or unxepected result using the mirt package with a 2pl model for binary variables

Question

I'm working with 5 variables related to "child and family health". My model is composed of items such as "Do you have a child with a learning or behavior problem?" and "Do you and your family members have health insurance or access to regular medical and dental care?" My model is well fitted: TLI 0.92 However, the coefficients that I've obtained showed that one item (cf2) has the difficulty at 31.375. I'm not sure if something is going under my radar and I'm wondering if someone could explain these results.

This is the simulated data, just to give you the chance to run my code

df2 = structure(list(cf1 = c(0, 0, 0, 10, 0, 0, 0, 10, 10, 0, 10, 0, 
                             0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                             0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                             0, 0, 0, 0, 10, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 
                             0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 
                             10, 0, 0, 0, 0, 0), cf2 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 
                                                         0, 0, 0, 0, 0, 10, 0, 10, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                         0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                         0, 10, 0, 0, 0, 0, 0), cf3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                                                        0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                                                        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                                                        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                                                        10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                                                        0, 0, 0, 0, 0, 0, 10, 0), cf4 = c(0, 10, 0, 0, 10, 0, 0, 10, 
                                                                                                                          10, 0, 10, 0, 10, 10, 0, 10, 0, 10, 0, 0, 0, 0, 0, 10, 10, 10, 
                                                                                                                          10, 10, 0, 0, 0, 0, 0, 10, 0, 0, 10, 0, 0, 0, 0, 0, 0, 10, 0, 
                                                                                                                          10, 0, 0, 0, 10, 10, 0, 10, 0, 0, 10, 10, 0, 10, 0, 0, 0, 0, 
                                                                                                                          0, 10, 0, 10, 10, 0, 10, 0, 10, 0, 10, 0, 0, 0, 0, 10, 10, 10, 
                                                                                                                          0, 10, 10, 10, 0, 0, 10, 10, 10, 0, 0, 0, 10, 0, 10, 0, 0, 10, 
                                                                                                                          0), cf5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 10, 0, 0, 0, 
                                                                                                                                      0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                                                                                                      0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 
                                                                                                                                      0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                                                                                                      0, 0, 0, 0, 0, 0, 0, 10, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
                                                                                                                                      0, 0, 0, 0)), row.names = c(NA, -100L), class = c("tbl_df", "tbl", 
                                                                                                                                                                                        "data.frame"))


mod_2pl_cf <- mirt(data = df2, 
                   model = 1,  
                   itemtype = "2PL"
)
coef(mod_2pl_cf, simplify=T, 
     IRTpars= TRUE)