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: "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction"
  Abstract: We report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of consistent decision trees and the factors that affect the accuracy of individual trees. In particular, we investigated the relationship between the size of a decision tree consistent with some training data and the accuracy of the tree on test data. The experiments were performed on a massively parallel Maspar computer. The results of the experiments on several artificial and two real world problems indicate that, for many of the problems investigated, smaller consistent decision trees are on average less accurate than the average accuracy of slightly larger trees.

Connected through hyperedge HE-1 (induced by source paper "What should be minimized in a decision tree: A re-examination"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "In Defense of C4.5: Notes on Learning One-Level Decision Trees"):
  with members:
    - "Induction of One-Level Decision Trees"
    - "Learning to Represent Codons: A Challenge Problem for Constructive Induction"

Connected through hyperedge HE-3 (induced by source paper "Lookahead and Pathology in Decision Tree Induction"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "Induction of Oblique Decision Trees"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"

Connected through hyperedge HE-4 (induced by source paper "Learning Decision Lists Using Homogeneous Rules"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"
    - "Combining Data Mining and Machine Learning for Effective User Profiling"
    - "Data Mining and Knowledge Discovery,  Adaptive Fraud Detection"

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