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: "In Defense of C4.5: Notes on Learning One-Level Decision Trees"
  Abstract: We discuss the implications of Holte's recently-published article, which demonstrated that on the most commonly used data very simple classification rules are almost as accurate as decision trees produced by Quinlan's C4.5. We consider, in particular, what is the significance of Holte's results for the future of top-down induction of decision trees. To an extent, Holte questioned the sense of further research on multilevel decision tree learning. We go in detail through all the parts of Holte's study. We try to put the results into perspective. We argue that the (in absolute terms) small difference in accuracy between 1R and C4.5 that was witnessed by Holte is still significant. We claim that C4.5 possesses additional accuracy-related advantages over 1R. In addition we discuss the representativeness of the databases used by Holte. We compare empirically the optimal accuracies of multilevel and one-level decision trees and observe some significant differences. We point out several deficien cies of limited-complexity classifiers.

Connected through hyperedge HE-1 (induced by source paper "Learning to Represent Codons: A Challenge Problem for Constructive Induction"):
  with members:
    - "Learning Sparse Perceptrons"
    - "Learning to Predict Reading Frames in E. coli DNA Sequences"
    - "EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS"

Connected through hyperedge HE-2 (induced by source paper "Induction of One-Level Decision Trees"):
  with members:
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction"):
  with members:
    - "Learning Decision Lists Using Homogeneous Rules"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "What should be minimized in a decision tree: A re-examination"
    - "Bias and the Quantification of Stability Bias and the Quantification of Stability Bias and the"

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