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 "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).

Includes members:
  Member "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "An intelligent search method using Inductive Logic Programming")
  Member "Multi-class problems and discretization in ICL Extended abstract":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Rule Induction with CN2: Some Recent Improvements")
  Member "Applications of a logical discovery engine":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Application of Clausal Discovery to Temporal Databases")
  Member "Learning with Abduction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "The ILP description learning problem: Towards a general model-level definition of data mining in ILP")
  Member "Inductive Constraint Logic and the Mutagenesis Problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Applications of a logical discovery engine")
  Member "Relational Knowledge Discovery in Databases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Inverse entailment and Progol")
  Member "The ILP description learning problem: Towards a general model-level definition of data mining in ILP":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Learning with Abduction")

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: Rule_Learning