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: "Relational Knowledge Discovery in Databases"
  Abstract: In this paper, we indicate some possible applications of ILP or similar techniques in the knowledge discovery field, and then discuss several methods for adapting and linking ILP-systems to relational database systems. The proposed methods range from "pure ILP" to "based on techniques originating in ILP". We show that it is both easy and advantageous to adapt ILP-systems in this way.

Connected through hyperedge HE-1 (induced by source paper "Inverse entailment and Progol"):
  with members:
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Predicting Ordinal Classes in ILP"
    - "Learning Trees and Rules with Set-valued Features"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"
    - "Extraction of Meta-Knowledge to Restrict the Hypothesis Space for ILP Systems incorporating them in FOIL."
    - "Which Hypotheses Can Be Found with Inverse Entailment? -Extended Abstract"
    - "Bottom-up induction of logic programs with more than one recursive clause"

Connected through hyperedge HE-2 (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"
    - "Inductive Constraint Logic and the Mutagenesis Problem"
    - "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