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: "New Methods for Competitive Coevolution"
  Abstract: We consider "competitive coevolution," in which fitness is based on direct competition among individuals selected from two independently evolving populations of "hosts" and "parasites." Competitive coevolution can lead to an "arms race," in which the two populations reciprocally drive one another to increasing levels of performance and complexity. We use the games of Nim and 3-D Tic-Tac-Toe as test problems to explore three new techniques in competitive coevolution. "Competitive fitness sharing" changes the way fitness is measured, "shared sampling" provides a method for selecting a strong, diverse set of parasites, and the "hall of fame" encourages arms races by saving good individuals from prior generations. We provide several different motivations for these methods, and mathematical insights into their use. Experimental comparisons are done, and a detailed analysis of these experiments is presented in terms of testing issues, diversity, extinction, arms race progress measurements, and drift.

Connected through hyperedge HE-1 (induced by source paper "17 Massively Parallel Genetic Programming"):
  with members:
    - "A Genome Compiler for High Performance Genetic Programming"

Connected through hyperedge HE-2 (induced by source paper "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"):
  with members:
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"
    - "Some Training Subset Selection Methods for Supervised Learning in Genetic Programming"

Connected through hyperedge HE-3 (induced by source paper "Automatic Modularization by Speciation"):
  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-4 (induced by source paper "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"):
  with members:
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Some Training Subset Selection Methods for Supervised Learning in Genetic Programming"
    - "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"

Connected through hyperedge HE-5 (induced by source paper "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"

Connected through hyperedge HE-6 (induced by source paper "Dynamics of Co-evolutionary Learning"):
  with members:
    (none)

Connected through hyperedge HE-7 (induced by source paper "A Competitive Approach to Game Learning"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"

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