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: "Choosing Learning Strategies to Achieve Learning Goals"
  Abstract: In open world applications a number of machine-learning techniques may potentially apply to a given learning situation. The research presented here illustrates the complexity involved in automatically choosing an appropriate technique in a multistrategy learning system. It also constitutes a step toward a general computational solution to the learning-strategy selection problem. The approach is to treat learning-strategy selection as a separate planning problem with its own set of goals, as is the case with ordinary problem-solvers. Therefore, the management and pursuit of these learning goals becomes a central issue in learning, similar to the goal-management problems associated with traditional planning systems. This paper explores some issues, problems, and possible solutions in such a framework. Examples are presented from a multistrategy learning system called Meta-AQUA.

Connected through hyperedge HE-1 (induced by source paper "Abstract"):
  with members:
    - "Introspective reasoning using meta-explanations for multistrategy learning"
    - "A theory of questions and question asking"
    - "Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases"

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: Case_Based