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: "Evolving Complex Structures via Co- operative Coevolution"
  Abstract: A cooperative coevolutionary approach to learning complex structures is presented which, although preliminary in nature, appears to have a number of advantages over non-coevolutionary approaches. The cooperative coevolutionary approach encourages the parallel evolution of substructures which interact in useful ways to form more complex higher level structures. The architecture is designed to be general enough to permit the inclusion, if appropriate, of a priori knowledge in the form of initial biases towards particular kinds of decompositions. A brief summary of initial results obtained from testing this architecture in several problem domains is presented which shows a significant speedup over more traditional non-coevolutionary approaches.

Connected through hyperedge HE-1 (induced by source paper "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Automatic Modularization by Speciation"
    - "Self-Nonself Discrimination in a Computer"

Connected through hyperedge HE-2 (induced by source paper "A Coevolutionary Approach to Learning Sequential Decision Rules"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Knowledge-Based Genetic Learning"
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Automatic Modularization by Speciation"

Connected through hyperedge HE-3 (induced by source paper "A Cooperative Coevolutionary Approach to Function Optimization"):
  with members:
    - "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization"

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: Genetic_Algorithms