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 "Application of Clausal Discovery to Temporal Databases"):
  Abstract: Most of KDD applications consider databases as static objects, and however many databases are inherently temporal, i.e., they store the evolution of each object with the passage of time. Thus, regularities about the dynamics of these databases cannot be discovered as the current state might depend in some way on the previous states. To this end, a pre-processing of data is needed aimed at extracting relationships intimately connected to the temporal nature of data that will be make available to the discovery algorithm. The predicate logic language of ILP methods together with the recent advances as to ef ficiency makes them adequate for this task.

Includes members:
  Member "Applications of a logical discovery engine":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Inductive Constraint Logic")
  Member "The ILP description learning problem: Towards a general model-level definition of data mining in ILP":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning with Abduction")

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