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 "AN ENHANCER FOR REACTIVE PLANS"):
  Abstract: This paper describes our method for improving the comprehensibility, accuracy, and generality of reactive plans. A reactive plan is a set of reactive rules. Our method involves two phases: (1) formulate explanations of execution traces, and then (2) generate new reactive rules from the explanations. Since the explanation phase has been previously described, the primary focus of this paper is the rule generation phase. This latter phase consists of taking a subset of the explanations and using these explanations to generate a set of new reactive rules to add to the original set. The particular subset of the explanations that is chosen yields rules that provide new domain knowledge for handling knowledge gaps in the original rule set. The original rule set, in a complimentary manner, provides expertise to fill the gaps where the domain knowledge provided by the new rules is incomplete.

Includes members:
  Member "Learning Sequential Decision Rules Using Simulation Models and Competition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Improving Tactical Plans with Genetic Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "EVOLVING ROBOT BEHAVIORS")
  Member "LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ROBOT LEARNING WITH PARALLEL GENETIC ALGORITHMS ON NETWORKED COMPUTERS")
  Member "Explanations of Empirically Derived Reactive Plans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition")

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: Case_Based