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: "Rule-based Machine Learning Methods for Functional Prediction"
  Abstract: We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.

Connected through hyperedge HE-1 (induced by source paper "Structural Regression Trees"):
  with members:
    - "Covering vs. Divide-and-Conquer for Top-Down Induction of Logic Programs"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
    - "Employing Linear Regression in Regression Tree Leaves"

Connected through hyperedge HE-2 (induced by source paper "Combining estimates in regression and classification"):
  with members:
    - "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Stacked Density Estimation"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"

Connected through hyperedge HE-3 (induced by source paper "Search-based Class Discretization"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"

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