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: "Lookahead and Pathology in Decision Tree Induction"
  Abstract: The standard approach to decision tree induction is a top-down, greedy algorithm that makes locally optimal, irrevocable decisions at each node of a tree. In this paper, we empirically study an alternative approach, in which the algorithms use one-level lookahead to decide what test to use at a node. We systematically compare, using a very large number of real and artificial data sets, the quality of decision trees induced by the greedy approach to that of trees induced using lookahead. The main observations from our experiments are: (i) the greedy approach consistently produced trees that were just as accurate as trees produced with the much more expensive lookahead step; and (ii) we observed many instances of pathology, i.e., lookahead produced trees that were both larger and less accurate than trees produced without it.

Connected through hyperedge HE-1 (induced by source paper "Induction of Oblique Decision Trees"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "An Improved Algorithm for Incremental Induction of Decision Trees"
    - "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Learning When Reformulation is Appropriate for Iterative Design"

Connected through hyperedge HE-2 (induced by source paper "Decision Tree Induction: How Effective is the Greedy Heuristic?"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"

Connected through hyperedge HE-3 (induced by source paper "A System for Induction of Oblique Decision Trees"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Geometry in Learning"
    - "A Decision Tree System for Finding Genes in DNA"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-4 (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"
    - "In Defense of C4.5: Notes on Learning One-Level Decision Trees"
    - "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