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: "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.

Connected through hyperedge HE-1 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems"):
  with members:
    - "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "Logarithmic-Time Updates and Queries in Probabilistic Networks"

Connected through hyperedge HE-2 (induced by source paper "Learning to Represent Codons: A Challenge Problem for Constructive Induction"):
  with members:
    - "Learning Sparse Perceptrons"
    - "Learning to Predict Reading Frames in E. coli DNA Sequences"
    - "In Defense of C4.5: Notes on Learning One-Level Decision Trees"

Connected through hyperedge HE-3 (induced by source paper "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"):
  with members:
    - "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Dynamical Selection of Learning Algorithms"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"

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