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: "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
  Abstract: Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of relevant features rely on cross-validation. As an alternative, we present the application of a particular Minimum Description Length (MDL) measure to the task of feature subset selection. Using the MDL principle allows taking into account all of the available data at once. The new measure is information-theoretically plausible and yet still simple and therefore efficiently computable. We show empirically that this new method for judging the value of feature subsets is more efficient than and performs at least as well as methods based on cross-validation. Domains with both a large number of training examples and a large number of possible features yield the biggest gains in efficiency. Thus our new approach seems to scale up better to large learning problems than previous methods.

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"
    - "Oblivious Decision Trees and Abstract Cases"
    - "Generalizing from Case Studies: A Case Study"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "On the Greediness of Feature Selection Algorithms"

Connected through hyperedge HE-2 (induced by source paper "The Power of Decision Tables"):
  with members:
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "Induction of Condensed Determinations"
    - "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-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: Theory