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: "Using Decision Trees to Improve Case-Based Learning"
  Abstract: This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task performed by decision tree algorithms and the flexible prediction task performed by conceptual clustering systems. In semi-flexible prediction, learning should improve prediction of a specific set of features known a priori rather than a single known feature (as in classification) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge.

Connected through hyperedge HE-1 (induced by source paper "Refining Conversational Case Libraries"):
  with members:
    - "NACODAE: Navy Conversational Decision Aids Environment"
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Learning to Refine Case Libraries:"
    - "Simplifying Decision Trees: A Survey"
    - "Correcting for Length Biasing in Conversational Case Scoring"
    - "A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner"
    - "Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning"

Connected through hyperedge HE-2 (induced by source paper "Learning to Refine Case Libraries:"):
  with members:
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Refining Conversational Case Libraries"

Connected through hyperedge HE-3 (induced by source paper "Case-Based Sonogram Classification"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Concept Learning and Flexible Weighting"

Connected through hyperedge HE-4 (induced by source paper "Error-Correcting Output Coding Corrects Bias and Variance"):
  with members:
    - "Combining estimates in regression and classification"
    - "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"

Connected through hyperedge HE-5 (induced by source paper "Oblivious Decision Trees and Abstract Cases"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Induction of Condensed Determinations"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"

Connected through hyperedge HE-6 (induced by source paper "Irrelevant Features and the Subset Selection Problem"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "An Optimized Theory Revision Module"
    - "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"

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