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 Teamwork and Coordination with Genetic Programming"
  Abstract: Some problems can be solved only by multi-agent teams. In using genetic programming to produce such teams, one faces several design decisions. First, there are questions of team diversity and of breeding strategy. In one commonly used scheme, teams consist of clones of single individuals; these individuals breed in the normal way and are cloned to form teams during fitness evaluation. In contrast, teams could also consist of distinct individuals. In this case one can either allow free interbreeding between members of different teams, or one can restrict interbreeding in various ways. A second design decision concerns the types of coordination-facilitating mechanisms provided to individual team members; these range from sensors of various sorts to complex communication systems. This paper examines three breeding strategies (clones, free, and restricted) and three coordination mechanisms (none, deictic sensing, and name-based sensing) for evolving teams of agents in the Serengeti world, a simple predator/prey environment. Among the conclusions are the fact that a simple form of restricted interbreeding outperforms free interbreeding in all teams with distinct individuals, and the fact that name-based sensing consistently outperforms deictic sensing.

Connected through hyperedge HE-1 (induced by source paper "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"):
  with members:
    - "Evolution of Iteration in Genetic Programming D a v d A The solution to many"
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "20 Data Structures and Genetic Programming  two techniques for reducing run time."
    - "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "Cultural Transmission of Information in Genetic Programming"
    - "Culture Enhances the Evolvability of Cognition"
    - "Neural Programming and an Internal Reinforcement Policy"

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