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: "A Cooperative Coevolutionary Approach to Function Optimization"
  Abstract: A general model for the coevolution of cooperating species is presented. This model is instantiated and tested in the domain of function optimization, and compared with a traditional GA-based function optimizer. The results are encouraging in two respects. They suggest ways in which the performance of GA and other EA-based optimizers can be improved, and they suggest a new approach to evolving complex structures such as neural networks and rule sets.

Connected through hyperedge HE-1 (induced by source paper "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic algorithms with multi-parent recombination"
    - "Multi-parent Recombination"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization"):
  with members:
    - "Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition"
    - "Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition"

Connected through hyperedge HE-3 (induced by source paper "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"
    - "Genetic algorithms with multi-parent recombination"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

Connected through hyperedge HE-4 (induced by source paper "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"):
  with members:
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

Connected through hyperedge HE-5 (induced by source paper "Evolving Complex Structures via Co- operative Coevolution"):
  with members:
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

Connected through hyperedge HE-6 (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"
    - "Automatic Modularization by Speciation"
    - "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