Consider a sofi$-$margin SVM with a linear kemel and no slack variables, trained on $n$ points. The number of support vectors returned is $k.$ By adding one extra point to the dataset and retraining the classifier, what is the maximum possible number of support vectors that ean be retumed $($tuning parameter $C?$

$k$

$n$

$n+1$

$k+1$