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: "What do Constructive Learners Really Learn?"
  Abstract: In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This may be necessary if the initial representation is inadequate or inappropriate. However, the distinction between constructive and non-constructive methods appears to be highly ambiguous. Several conventional definitions of the process of constructive induction appear to include all conceivable learning processes. In this paper I argue that the process of constructive learning should be identified with that of relational learning (i.e., I suggest that

Connected through hyperedge HE-1 (induced by source paper "Rule Induction with CN2: Some Recent Improvements"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"
    - "Using Qualitative Models to Guide Inductive Learning"
    - "Case-Based Sonogram Classification"
    - "Relating Relational Learning Algorithms"
    - "Generalizing from Case Studies: A Case Study"
    - "Incremental Reduced Error Pruning"
    - "Unsupervised Constructive Learning"
    - "Fossil: A Robust Relational Learner"

Connected through hyperedge HE-2 (induced by source paper "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"):
  with members:
    - "Constructing Conjunctions using Systematic Search on Decision Trees"
    - "Constructing Nominal Xof-N Attributes"
    - "Extending Theory Refinement to M-of-N Rules"
    - "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach"
    - "Unsupervised Constructive Learning"
    - "Parity: The Problem that Won't Go Away"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"
    - "CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES"

Connected through hyperedge HE-3 (induced by source paper "A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks"):
  with members:
    - "Unsupervised Constructive Learning"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"
    - "Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a"

Connected through hyperedge HE-4 (induced by source paper "Constructive Induction Using a Non-Greedy Strategy for Feature Selection"):
  with members:
    - "Unsupervised Constructive Learning"

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