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 "EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS"):
  Abstract: The relative performance of different methods for classifier learning varies across domains. Some recent Instance Based Learning (IBL) methods, such as IB1-MVDM* 10 , use similarity measures based on conditional class probabilities. These probabilities are a key component of Naive Bayes methods. Given this commonality of approach, it is of interest to consider how the differences between the two methods are linked to their relative performance in different domains. Here we interpret Naive Bayes in an IBL like framework, identifying differences between Naive Bayes and IB1-MVDM* in this framework. Experiments on variants of IB1-MVDM* that lie between it and Naive Bayes in the framework are conducted on sixteen domains. The results strongly suggest that the relative performance of Naive Bayes and IB1-MVDM* is linked to the extent to which each class can be satisfactorily represented by a single instance in the IBL framework. However, this is not the only factor that appears significant.

Includes members:
  Member "Towards a Better Understanding of Memory-Based Reasoning Systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Logarithmic-Time Updates and Queries in Probabilistic Networks")
  Member "Learning to Represent Codons: A Challenge Problem for Constructive Induction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning to Predict Reading Frames in E. coli DNA Sequences")
  Member "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems")

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