Suppose we train a hard-margin linear SVM on n > 100 datapoints in R₂, yielding a hyperplane with exactly 2 support vectors. If we add one more datapoint and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n + 1 points are linearly separable)? Select one of: {2, 3, n, n + 1}. Optional: draw a case that justifies your answer
