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: "Mutation Rates as Adaptations"
  Abstract: In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of a gene for the mutation rate of the individual. This allowed the mutation rate itself to evolve in a lineage. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that different genes may have evolved different mutation rates as adaptations to the varying pressures of interactions with other genes.

Connected through hyperedge HE-1 (induced by source paper "Optimal Mutation Rates in Genetic Search"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Coevolution of Mutation Rates"
    - "A Survey of Evolution Strategies"
    - "Between-host evolution of mutation-rate and within-host evolution of virulence."
    - "Simulated Annealing for Hard Satisfiability Problems"

Connected through hyperedge HE-2 (induced by source paper "Between-host evolution of mutation-rate and within-host evolution of virulence."):
  with members:
    - "Optimal Mutation Rates in Genetic Search"

Connected through hyperedge HE-3 (induced by source paper "The Coevolution of Mutation Rates"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Optimal Mutation Rates in Genetic Search"

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