A decision tree is a classification model. You can train a decision tree on a training set $D$ in order to predict the labels of records in a test set. $m$ is the possible number of labels. E.g. $m = 2$ you have a binary class problem, for example classifying patients who might either have or not have a disease; $m > 2$ you have multi-class problem, for example if you had to classify news in "politics", "sport", and "culture".
Think about how decision trees work. I wrote an explanation here. The hint I can give to you is that in your homework attributes are not relevant to the class. Also, think about the way decision trees classify records on leaf nodes.