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: "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.

Connected through hyperedge HE-1 (induced by source paper "Applications of a logical discovery engine"):
  with members:
    - "Inductive Database Design"
    - "Inductive Constraint Logic"
    - "Inductive Constraint Logic and the Mutagenesis Problem"

Connected through hyperedge HE-2 (induced by source paper "The ILP description learning problem: Towards a general model-level definition of data mining in ILP"):
  with members:
    - "Learning with Abduction"
    - "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