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: "Learning with Abduction"
  Abstract: We investigate how abduction and induction can be integrated into a common learning framework through the notion of Abductive Concept Learning (ACL). ACL is an extension of Inductive Logic Programming (ILP) to the case in which both the background and the target theory are abductive logic programs and where an abductive notion of entailment is used as the coverage relation. In this framework, it is then possible to learn with incomplete information about the examples by exploiting the hypothetical reasoning of abduction. The paper presents the basic framework of ACL with its main characteristics and illustrates its potential in addressing several problems in ILP such as learning with incomplete information and multiple predicate learning. An algorithm for ACL is developed by suitably extending the top-down ILP method for concept learning and integrating this with an abductive proof procedure for Abductive Logic Programming (ALP). A prototype system has been developed and applied to learning problems with incomplete information. The particular role of integrity constraints in ACL is investigated showing ACL as a hybrid learning framework that integrates the explanatory (discriminant) and descriptive (characteristic) settings of ILP.

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

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"
    - "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"
    - "Applications of a logical discovery engine"

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