human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Instance Pruning Techniques"
  Abstract: The nearest neighbor algorithm and its derivatives are often quite successful at learning a concept from a training set and providing good generalization on subsequent input vectors. However, these techniques often retain the entire training set in memory, resulting in large memory requirements and slow execution speed, as well as a sensitivity to noise. This paper provides a discussion of issues related to reducing the number of instances retained in memory while maintaining (and sometimes improving) generalization accuracy, and mentions algorithms other researchers have used to address this problem. It presents three intuitive noise-tolerant algorithms that can be used to prune instances from the training set. In experiments on 29 applications, the algorithm that achieves the highest reduction in storage also results in the highest generalization accuracy of the three methods.

Connected through hyperedge HE-1 (induced by source paper "Bias and the Probability of Generalization"):
  with members:
    - "Generalizing from Case Studies: A Case Study"

Connected through hyperedge HE-2 (induced by source paper "In Proceedings of the 1997 Sian Kaan International Workshop on Neural Networks and Neurocontrol. Real-Valued"):
  with members:
    - "An Overview of Evolutionary Computation"

Question: Please tell me which class the center node 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