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: "The ILP description learning problem: Towards a general model-level definition of data mining in ILP"
  Abstract: stefan.wrobel@gmd.de, saso.dzeroski@gmd.de Proc. FGML-95, Annual Workshop of the GI Special Interest Group Machine Learning (GI FG 1.1.3), ed. K. Morik and J. Herrmann, Research Report 580, Univ.Dortmund, 1995. Abstract The task of discovering interesting regularities in (large) sets of data (data mining, knowledge discovery) has recently met with increased interest in Machine Learning in general and in Inductive Logic Programming (ILP) in particular. However, while there is a widely accepted definition for the task of concept learning from examples in ILP, definitions for the data mining task have been proposed only recently. In this paper, we examine these so-called "non-monotonic semantics" definitions and show that non-monotonicity is only an incidental property of the data mining learning task, and that this task makes perfect sense without such an assumption. We therefore introduce and define a generalized definition of the data mining task called the ILP description learning problem and discuss its properties and relation to the traditional concept learning (prediction) learning problem. Since our characterization is entirely on the level of models, the definition applies independently of the chosen hypothesis language.

Connected through hyperedge HE-1 (induced by source paper "Learning with Abduction"):
  with members:
    - "Inductive Database Design"
    - "Inductive Constraint Logic"

Connected through hyperedge HE-2 (induced by source paper "Application of Clausal Discovery to Temporal Databases"):
  with members:
    - "Applications of a logical discovery engine"

Connected through hyperedge HE-3 (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"
    - "Relational Knowledge Discovery in Databases"

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