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: "Induction of Condensed Determinations"
  Abstract: In this paper we suggest determinations as a representation of knowledge that should be easy to understand. We briefly review determinations, which can be displayed in a tabular format, and their use in prediction, which involves a simple matching process. We describe ConDet, an algorithm that uses feature selection to construct determinations from training data, augmented by a condensation process that collapses rows to produce simpler structures. We report experiments that show condensation reduces complexity with no loss of accuracy, then discuss ConDet's relation to other work and outline directions for future studies.

Connected through hyperedge HE-1 (induced by source paper "The Power of Decision Tables"):
  with members:
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
    - "Decision Tree Induction Based on Efficient Tree Restructuring"
    - "Targeting Business Users with Decision Table Classifiers"
    - "A Yardstick for the Evaluation of Case-Based Classifiers"

Connected through hyperedge HE-2 (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"
    - "Using Decision Trees to Improve Case-Based Learning"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"

Connected through hyperedge HE-3 (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