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: "Feature Subset Selection as Search with Probabilistic Estimates"
  Abstract: Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading off accuracy of estimates for increased state exploration. We show how recent feature subset selection algorithms in the machine learning literature fit into this search problem as simple hill climbing approaches, and conduct a small experiment using a best-first search technique.

Connected through hyperedge HE-1 (induced by source paper "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Using Errors to Create Piecewise Learnable Partitions"
    - "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"
    - "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"

Connected through hyperedge HE-2 (induced by source paper "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "MLC A Machine Learning Library in C"
    - "Selection of Relevant Features and Examples in Machine Learning"
    - "Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier"

Connected through hyperedge HE-3 (induced by source paper "Automatic Parameter Selection by Minimizing Estimated Error"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
    - "The Power of Decision Tables"
    - "Learning Symbolic Rules Using Artificial Neural Networks"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"

Connected through hyperedge HE-4 (induced by source paper "Estimating Attributes: Analysis and Extensions of RELIEF"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "An adaptation of Relief for attribute estimation in regression"
    - "Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning"
    - "Context-sensitive attribute estimation in regression"
    - "Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"
    - "Naive Bayesian classifier within ILP-R"
    - "Linear Space Induction in First Order Logic with RELIEFF"

Connected through hyperedge HE-5 (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"
    - "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"
    - "The Effective Size of a Neural Network: A Principal Component Approach"

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