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 "Lookahead and Discretization in ILP"):
  Abstract: We present and evaluate two methods for improving the performance of ILP systems. One of them is discretization of numerical attributes, based on Fayyad and Irani's text [9], but adapted and extended in such a way that it can cope with some aspects of discretization that only occur in relational learning problems (when indeterminate literals occur). The second technique is lookahead. It is a well-known problem in ILP that a learner cannot always assess the quality of a refinement without knowing which refinements will be enabled afterwards, i.e. without looking ahead in the refinement lattice. We present a simple method for specifying when lookahead is to be used, and what kind of lookahead is interesting. Both the discretization and lookahead techniques are evaluated experimentally. The results show that both techniques improve the quality of the induced theory, while computational costs are acceptable.

Includes members:
  Member "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction")
  Member "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "Rule Induction with CN2: Some Recent Improvements")
  Member "Top-down Induction of Logical Decision Trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Generating Declarative Language Bias for Top-Down ILP Algorithms")

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