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