human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Improving Minority Class Prediction Using Case-Specific Feature Weights"):
  Abstract: This paper addresses the problem of handling skewed class distributions within the case-based learning (CBL) framework. We first present as a baseline an information-gain-weighted CBL algorithm and apply it to three data sets from natural language processing (NLP) with skewed class distributions. Although overall performance of the baseline CBL algorithm is good, we show that the algorithm exhibits poor performance on minority class instances. We then present two CBL algorithms designed to improve the performance of minority class predictions. Each variation creates test-case-specific feature weights by first observing the path taken by the test case in a decision tree created for the learning task, and then using path-specific information gain values to create an appropriate weight vector for use during case retrieval. When applied to the NLP data sets, the algorithms are shown to significantly increase the accuracy of minority class predictions while maintaining or improving overall classification accuracy.

Includes members:
  Member "Concept Learning and Flexible Weighting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Case_Based