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: "Optimization by Means of Genetic Algorithms"
  Abstract: Genetic Algorithms (GAs) are powerful heuristic search strategies based upon a simple model of organic evolution. The basic working scheme of GAs as developed by Holland [Hol75] is described within this paper in a formal way, and extensions based upon the second-level learning principle for strategy parameters as introduced in Evolution Strategies (ESs) are proposed. First experimental results concerning this extension of GAs are also reported.

Connected through hyperedge HE-1 (induced by source paper "Genetic Self-Learning"):
  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"
    - "Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation"

Connected through hyperedge HE-2 (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-3 (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."
    - "Genetic Self-Learning"
    - "A Survey of Evolution Strategies"
    - "Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first"
    - "Pruning backpropagation neural networks using modern stochastic optimization techniques"

Connected through hyperedge HE-4 (induced by source paper "Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first"):
  with members:
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Evolution in Time and Space The Parallel Genetic Algorithm"
    - "A Survey of Evolution Strategies"
    - "Strategy Adaptation by Competing Subpopulations"

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