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"
  Abstract: A novel approach to learning first order logic formulae from positive and negative examples is presented. Whereas present inductive logic programming systems employ examples as true and false ground facts (or clauses), we view examples as interpretations which are true or false for the target theory. This viewpoint allows to reconcile the inductive logic programming paradigm with classical attribute value learning in the sense that the latter is a special case of the former. Because of this property, we are able to adapt AQ and CN2 type algorithms in order to enable learning of full first order formulae. However, whereas classical learning techniques have concentrated on concept representations in disjunctive normal form, we will use a clausal representation, which corresponds to a conjuctive normal form where each conjunct forms a constraint on positive examples. This representation duality reverses also the role of positive and negative examples, both in the heuristics and in the algorithm. The resulting theory is incorporated in a system named ICL (Inductive Constraint Logic).

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 and the Mutagenesis Problem"

Connected through hyperedge HE-2 (induced by source paper "Learning with Abduction"):
  with members:
    - "Inductive Database Design"
    - "The ILP description learning problem: Towards a general model-level definition of data mining in ILP"

Connected through hyperedge HE-3 (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 and the Mutagenesis Problem"
    - "Lookahead and Discretization in ILP"

Connected through hyperedge HE-4 (induced by source paper "Relational Knowledge Discovery in Databases"):
  with members:
    - "Inverse entailment and Progol"

Connected through hyperedge HE-5 (induced by source paper "The ILP description learning problem: Towards a general model-level definition of data mining in ILP"):
  with members:
    - "Learning with Abduction"
    - "Application of Clausal Discovery to Temporal Databases"

Connected through hyperedge HE-6 (induced by source paper "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "An intelligent search method using Inductive Logic Programming"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"
    - "Lookahead and Discretization in ILP"

Connected through hyperedge HE-7 (induced by source paper "Inductive Constraint Logic and the Mutagenesis Problem"):
  with members:
    - "Multi-class problems and discretization in ICL Extended abstract"
    - "Applications of a logical discovery engine"

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