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: "Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first"
  Abstract: In this paper a new approach is presented, which transfers a basic idea from Evolution Strategies (ESs) to GAs. Mutation rates are changed into endogeneous items which are adapting during the search process. First experimental results are presented, which indicate that environment-dependent self-adaptation of appropriate settings for the mutation rate is possible even for GAs.

Connected through hyperedge HE-1 (induced by source paper "Evolution in Time and Space The Parallel Genetic Algorithm"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Island Model Genetic Algorithms and Linearly Separable Problems"
    - "A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3CNF Problems"
    - "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"
    - "Genetic Algorithm Programming Environments"
    - "A Survey of Parallel Genetic Algorithms"
    - "Staged Hybrid Genetic Search for Seismic Data Imaging"
    - "The Schema Theorem and Price's Theorem"

Connected through hyperedge HE-2 (induced by source paper "Strategy Adaptation by Competing Subpopulations"):
  with members:
    - "A Survey of Evolution Strategies"

Connected through hyperedge HE-3 (induced by source paper "Optimization by Means of Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Genetic Self-Learning"

Connected through hyperedge HE-4 (induced by source paper "Extended Selection Mechanisms in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Optimization by Means of Genetic Algorithms"
    - "Genetic Self-Learning"
    - "A Survey of Evolution Strategies"
    - "Pruning backpropagation neural networks using modern stochastic optimization techniques"

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"
    - "Optimal Mutation Rates in Genetic Search"
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation"

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