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: "Cooperation of Data-driven and Model-based Induction Methods for Relational Learning"
  Abstract: Inductive learning in relational domains has been shown to be intractable in general. Many approaches to this task have been suggested nevertheless; all in some way restrict the hypothesis space searched. They can be roughly divided into two groups: data-driven, where the restriction is encoded into the algorithm, and model-based, where the restrictions are made more or less explicit with some form of declarative bias. This paper describes Incy, an inductive learner that seeks to combine aspects of both approaches. Incy is initially data-driven, using examples and background knowledge to put forth and specialize hypotheses based on the "connectivity" of the data at hand. It is model-driven in that hypotheses are abstracted into rule models, which are used both for control decisions in the data-driven phase and for model-guided induction. Key Words: Inductive learning in relational domains, cooperation of data-driven and model-guided methods, implicit and declarative bias.

Connected through hyperedge HE-1 (induced by source paper "What online Machine Learning can do for Knowledge Acquisition A Case Study"):
  with members:
    - "An Efficient Subsumption Algorithm for Inductive Logic Programming"

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