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 "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"):
  Abstract: We discuss an approach to constructing composite features during the induction of decision trees. The composite features correspond to m-of-n concepts. There are three goals of this research. First, we explore a family of greedy methods for building m-of-n concepts (one of which, GS, is described in this paper). Second, we show how these concepts can be formed as internal nodes of decision trees, serving as a bias to the learner. Finally, we evaluate the method on several artificially generated and naturally occurring data sets to determine the effects of this bias.

Includes members:
  Member "Constructing Conjunctions using Systematic Search on Decision Trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Multivariate versus Univariate Decision Trees")
  Member "Constructing Nominal Xof-N Attributes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction")
  Member "Extending Theory Refinement to M-of-N Rules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases")
  Member "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Using Qualitative Models to Guide Inductive Learning")
  Member "What do Constructive Learners Really Learn?":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Rule Induction with CN2: Some Recent Improvements")
  Member "Unsupervised Constructive Learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Rule Induction with CN2: Some Recent Improvements")
  Member "Parity: The Problem that Won't Go Away":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules")
  Member "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks")

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