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: "Oblivious Decision Trees and Abstract Cases"
  Abstract: In this paper, we address the problem of case-based learning in the presence of irrelevant features. We review previous work on attribute selection and present a new algorithm, Oblivion, that carries out greedy pruning of oblivious decision trees, which effectively store a set of abstract cases in memory. We hypothesize that this approach will efficiently identify relevant features even when they interact, as in parity concepts. We report experimental results on artificial domains that support this hypothesis, and experiments with natural domains that show improvement in some cases but not others. In closing, we discuss the implications of our experiments, consider additional work on irrelevant features, and outline some directions for future research.

Connected through hyperedge HE-1 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Growing Simpler Decision Trees to Facilitate Knowledge Discovery"
    - "Automated model selection"
    - "Generalizing from Case Studies: A Case Study"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
    - "On the Greediness of Feature Selection Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Average-Case Analysis of a Nearest Neighbor Algorithm"):
  with members:
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "Induction of One-Level Decision Trees"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Inductive Bias in Case-Based Reasoning Systems"
    - "PAC Analyses of a `Similarity Learning' IBL Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Using Decision Trees to Improve Case-Based Learning"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Case-Based Sonogram Classification"
    - "Learning to Refine Case Libraries:"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "Refining Conversational Case Libraries"

Connected through hyperedge HE-4 (induced by source paper "Induction of Condensed Determinations"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "The Power of Decision Tables"

Connected through hyperedge HE-5 (induced by source paper "Fast NP Chunking Using Memory-Based Learning Techniques"):
  with members:
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

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