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 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.

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"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Automatic Modularization by Speciation"
    - "Evolving Complex Structures via Co- operative Coevolution"
    - "Self-Nonself Discrimination in a Computer"

Connected through hyperedge HE-2 (induced by source paper "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"):
  with members:
    - "Real-time Interactive Neuro-evolution"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems"
    - "2-D Pole Balancing with Recurrent Evolutionary Networks"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

Connected through hyperedge HE-3 (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"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Automatic Modularization by Speciation"
    - "Evolving Complex Structures via Co- operative Coevolution"

Connected through hyperedge HE-4 (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"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization"
    - "Evolving Complex Structures via Co- operative Coevolution"

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