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: "Applications of a logical discovery engine"
  Abstract: The clausal discovery engine claudien is presented. claudien discovers regularities in data and is a representative of the inductive logic programming paradigm. As such, it represents data and regularities by means of first order clausal theories. Because the search space of clausal theories is larger than that of attribute value representation, claudien also accepts as input a declarative specification of the language bias, which determines the set of syntactically well-formed regularities. Whereas other papers on claudien focuss on the semantics or logical problem specification of claudien, on the discovery algorithm, or the PAC-learning aspects, this paper wants to illustrate the power of the resulting technique. In order to achieve this aim, we show how claudien can be used to learn 1) integrity constraints in databases, 2) functional dependencies and determinations, 3) properties of sequences, 4) mixed quantitative and qualitative laws, 5) reverse engineering, and 6) classification rules.

Connected through hyperedge HE-1 (induced by source paper "Application of Clausal Discovery to Temporal Databases"):
  with members:
    - "The ILP description learning problem: Towards a general model-level definition of data mining in ILP"

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"
    - "Learning with Abduction"
    - "Inductive Constraint Logic and the Mutagenesis Problem"
    - "Relational Knowledge Discovery in Databases"
    - "The ILP description learning problem: Towards a general model-level definition of data mining in ILP"

Connected through hyperedge HE-3 (induced by source paper "Inductive Database Design"):
  with members:
    - "Dlab: A Declarative Language Bias Formalism"
    - "Learning with Abduction"

Connected through hyperedge HE-4 (induced by source paper "Inductive Constraint Logic and the Mutagenesis Problem"):
  with members:
    - "Multi-class problems and discretization in ICL Extended abstract"
    - "Inductive Constraint Logic"

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