Automatic short answer grading remains one of the key challenges of any dialog-based tutoring system due to the variability in the student answers. Typically, each question may have no or few expert authored exemplary answers which make it difficult to (1) generalize to all correct ways of answering the question, or (2) represent answers which are either partially correct or incorrect. In this paper, we propose an affinity propagation based clustering technique to obtain class-specific representative answers from the graded student answers. Our novelty lies in formulating the Scoring Rubric by incorporating class-specific representatives obtained after proposed clustering, selecting, and ranking of graded student answers. We experiment with baseline as well as state-of-the-art sentence-embedding based features to demonstrate the feature-agnostic utility of class-specific representative answers. Experimental evaluations on our large-scale industry dataset and a benchmarking dataset show that the Scoring Rubric significantly improves the classification performance of short answer grading.