human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Inductive Database Design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Applications of a logical discovery engine")
  Member "Inductive Constraint Logic":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Applications of a logical discovery engine")
  Member "The ILP description learning problem: Towards a general model-level definition of data mining in ILP":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Application of Clausal Discovery to Temporal Databases")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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