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: "Optimal Mutation Rates in Genetic Search"
  Abstract: The optimization of a single bit string by means of iterated mutation and selection of the best (a (1+1)-Genetic Algorithm) is discussed with respect to three simple fitness functions: The counting ones problem, a standard binary encoded integer, and a Gray coded integer optimization problem. A mutation rate schedule that is optimal with respect to the success probability of mutation is presented for each of the objective functions, and it turns out that the standard binary code can hamper the search process even in case of unimodal objective functions. While normally a mutation rate of 1=l (where l denotes the bit string length) is recommendable, our results indicate that a variation of the mutation rate is useful in cases where the fitness function is a multimodal pseudo-boolean function, where multimodality may be caused by the objective function as well as the encoding mechanism.

Connected through hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-2 (induced by source paper "Mutation Rates as Adaptations"):
  with members:
    - "The Coevolution of Mutation Rates"
    - "Between-host evolution of mutation-rate and within-host evolution of virulence."

Connected through hyperedge HE-3 (induced by source paper "Simulated Annealing for Hard Satisfiability Problems"):
  with members:
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
    - "A NN Algorithm for Boolean Satisfiability Problems"

Connected through hyperedge HE-4 (induced by source paper "Between-host evolution of mutation-rate and within-host evolution of virulence."):
  with members:
    - "Mutation Rates as Adaptations"

Connected through hyperedge HE-5 (induced by source paper "A Survey of Evolution Strategies"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "On The State of Evolutionary Computation"
    - "A Stochastic Search Approach to Grammar Induction"
    - "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"
    - "Evaluating Evolutionary Algorithms"
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation"
    - "A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction"

Connected through hyperedge HE-6 (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."
    - "Mutation Rates as Adaptations"

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