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 "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"):
  Abstract: We present a coevolutionary architecture for solving decomposable problems and apply it to the evolution of artificial neural networks. Although this work is preliminary in nature it has a number of advantages over non-coevolutionary approaches. The coevolutionary approach utilizes a divide-and-conquer technique in which species representing simpler subtasks are evolved in separate instances of a genetic algorithm executing in parallel. Collaborations among the species are formed representing complete solutions. Species are created dynamically as needed. Results are presented in which the coevolutionary architecture produces higher quality solutions in fewer evolutionary trials when compared with an alternative non-coevolutionary approach on the problem of evolving cascade networks for parity computation.

Includes members:
  Member "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance")
  Member "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "2-D Pole Balancing with Recurrent Evolutionary Networks")
  Member "A Coevolutionary Approach to Learning Sequential Decision Rules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks")
  Member "A Cooperative Coevolutionary Approach to Function Optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms")

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: Genetic_Algorithms