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: "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.

Connected through hyperedge HE-1 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction"):
  with members:
    - "Constructing Nominal Xof-N Attributes"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"

Connected through hyperedge HE-2 (induced by source paper "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"):
  with members:
    - "Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents:"
    - "Parity: The Problem that Won't Go Away"
    - "A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Extending Theory Refinement to M-of-N Rules"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"

Connected through hyperedge HE-4 (induced by source paper "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"):
  with members:
    - "Constructing Nominal Xof-N Attributes"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"
    - "Effects of Different Types of New Attribute on Constructive Induction"

Connected through hyperedge HE-5 (induced by source paper "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"):
  with members:
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Extracting Tree-Structured Representations of Trained Networks"
    - "Learning from Bad Data"

Connected through hyperedge HE-6 (induced by source paper "Constructing Nominal Xof-N Attributes"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"
    - "Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study"
    - "Effects of Different Types of New Attribute on Constructive Induction"

Connected through hyperedge HE-7 (induced by source paper "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach"):
  with members:
    - "Using Qualitative Models to Guide Inductive Learning"
    - "Investigating the Value of a Good Input Representation"

Connected through hyperedge HE-8 (induced by source paper "Parity: The Problem that Won't Go Away"):
  with members:
    - "Truth-from-Trash Learning and the Mobot"
    - "Separability is a Learner's Best Friend"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"

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