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: "A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms"
  Abstract: Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that k-NN's performance is highly sensitive to the definition of its distance function. Many k-NN variants have been proposed to reduce this sensitivity by parameterizing the distance function with feature weights. However, these variants have not been categorized nor empirically compared. This paper reviews a class of weight-setting methods for lazy learning algorithms. We introduce a framework for distinguishing these methods and empirically compare them. We observed four trends from our experiments and conducted further studies to highlight them. Our results suggest that methods which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings. We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others.

Connected through hyperedge HE-1 (induced by source paper "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"):
  with members:
    - "Formalising the knowledge content of case memory systems"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "A Yardstick for the Evaluation of Case-Based Classifiers"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"

Connected through hyperedge HE-2 (induced by source paper "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"):
  with members:
    - "Refining Conversational Case Libraries"
    - "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner"

Connected through hyperedge HE-3 (induced by source paper "PAC Analyses of a `Similarity Learning' IBL Algorithm"):
  with members:
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the"

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