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: "Case-Based Sonogram Classification"
  Abstract: This report replicates and extends results reported by Naval Air Warfare Center (NAWC) personnel on the automatic classification of sonar images. They used novel case-based reasoning systems in their empirical studies, but did not obtain comparative analyses using standard classification algorithms. Therefore, the quality of the NAWC results were unknown. We replicated the NAWC studies and also tested several other classifiers (i.e., both case-based and otherwise) from the machine learning literature. These comparisons and their ramifications are detailed in this paper. Next, we investigated Fala and Walker's two suggestions for future work (i.e., on combining their similarity functions and on an alternative case representation). Finally, we describe several ways to incorporate additional domain-specific knowledge when applying case-based classifiers to similar tasks.

Connected through hyperedge HE-1 (induced by source paper "Using Decision Trees to Improve Case-Based Learning"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Learning to Refine Case Libraries:"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "Oblivious Decision Trees and Abstract Cases"
    - "Refining Conversational Case Libraries"

Connected through hyperedge HE-2 (induced by source paper "Rule Induction with CN2: Some Recent Improvements"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"
    - "Using Qualitative Models to Guide Inductive Learning"
    - "Relating Relational Learning Algorithms"
    - "Generalizing from Case Studies: A Case Study"
    - "Incremental Reduced Error Pruning"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Fossil: A Robust Relational Learner"

Connected through hyperedge HE-3 (induced by source paper "Concept Learning and Flexible Weighting"):
  with members:
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "The Management of Context-Sensitive Features: A Review of Strategies"
    - "Improving Minority Class Prediction Using Case-Specific Feature Weights"

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