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: "Between-host evolution of mutation-rate and within-host evolution of virulence."
  Abstract: It has been recently realized that parasite virulence (the harm caused by parasites to their hosts) can be an adaptive trait. Selection for a particular level of virulence can happen either at at the level of between-host tradeoffs or as a result of short-sighted within-host competition. This paper describes some simulations which study the effect that modifier genes for changes in mutation rate have on suppressing this short-sighted development of virulence, and investigates the interaction between this and a simplified model of im mune clearance.

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"
    - "Mutation Rates as Adaptations"
    - "Simulated Annealing for Hard Satisfiability Problems"

Connected through hyperedge HE-2 (induced by source paper "Mutation Rates as Adaptations"):
  with members:
    - "Optimal Mutation Rates in Genetic Search"
    - "The Coevolution of Mutation Rates"

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