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: "Inductive Constraint Logic and the Mutagenesis Problem"
  Abstract: A novel approach to learning first order logic formulae from positive and negative examples is incorporated in a system named ICL (Inductive Constraint Logic). In ICL, examples are viewed as interpretations which are true or false for the target theory, whereas in present inductive logic programming systems, examples are true and false ground facts (or clauses). Furthermore, ICL uses a clausal representation, which corresponds to a conjunctive normal form where each conjunct forms a constraint on positive examples, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form. We present some experiments with this new system on the mutagenesis problem. These experiments illustrate some of the differences with other systems, and indicate that our approach should work at least as well as the more classical approaches.

Connected through hyperedge HE-1 (induced by source paper "Applications of a logical discovery engine"):
  with members:
    - "Inductive Database Design"
    - "Application of Clausal Discovery to Temporal Databases"
    - "Inductive Constraint Logic"

Connected through hyperedge HE-2 (induced by source paper "Multi-class problems and discretization in ICL Extended abstract"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Inductive Constraint Logic"
    - "Lookahead and Discretization in ILP"

Connected through hyperedge HE-3 (induced by source paper "Inductive Constraint Logic"):
  with members:
    - "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"
    - "Multi-class problems and discretization in ICL Extended abstract"
    - "Applications of a logical discovery engine"
    - "Learning with Abduction"
    - "Relational Knowledge Discovery in Databases"
    - "The ILP description learning problem: Towards a general model-level definition of data mining in ILP"

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: Rule_Learning