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: "On The State of Evolutionary Computation"
  Abstract: In the past few years the evolutionary computation landscape has been rapidly changing as a result of increased levels of interaction between various research groups and the injection of new ideas which challenge old tenets. The effect has been simultaneously exciting, invigorating, annoying, and bewildering to the old-timers as well as the new-comers to the field. Emerging out of all of this activity are the beginnings of some structure, some common themes, and some agreement on important open issues. We attempt to summarize these emergent properties in this paper.

Connected through hyperedge HE-1 (induced by source paper "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"):
  with members:
    - "Adapting Crossover in a Genetic Algorithm"
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"
    - "Using Problem Generators to Explore the Effects of Epistasis"
    - "On the Virtues of Parameterized Uniform Crossover"
    - "Crossover or Mutation?"
    - "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"

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 "Adapting Crossover in a Genetic Algorithm"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"

Connected through hyperedge HE-4 (induced by source paper "Dynamic Parameter Encoding for Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Representation and Evolution of Neural Networks"
    - "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"
    - "Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques"
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"
    - "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"

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."
    - "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"
    - "A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction"

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