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: "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.

Connected through hyperedge HE-1 (induced by source paper "Top-down Induction of Logical Decision Trees"):
  with members:
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"

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"
    - "Inductive Constraint Logic and the Mutagenesis Problem"

Connected through hyperedge HE-3 (induced by source paper "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"):
  with members:
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Generalizing from Case Studies: A Case Study"
    - "A Note on Learning from Multiple-Instance Examples"
    - "A Framework for Multiple-Instance Learning"
    - "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction"

Connected through hyperedge HE-4 (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"
    - "Inductive Constraint Logic"
    - "Generating Declarative Language Bias for Top-Down ILP Algorithms"

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