human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "A System for Induction of Oblique Decision Trees"):
  Abstract: This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.

Includes members:
  Member "Decision Tree Function Approximation in Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding")
  Member "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Machine Learning Research: Four Current Directions")
  Member "Lazy Induction Triggered by CBR":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Inductive Learning and Case-Based Reasoning")
  Member "Induction of Oblique Decision Trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")
  Member "Geometry in Learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS")
  Member "Lookahead and Pathology in Decision Tree Induction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Induction of Oblique Decision Trees")
  Member "A Decision Tree System for Finding Genes in DNA":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A Method for Identifying Splice Sites and Translational Start Sites in")
  Member "Decision Tree Induction: How Effective is the Greedy Heuristic?":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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