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: "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"
  Abstract: We compare Genetic Algorithms (GA) with a functional search method, Very Fast Simulated Reannealing (VFSR), that not only is efficient in its search strategy, but also is statistically guaranteed to find the function optima. GA previously has been demonstrated to be competitive with other standard Boltzmann-type simulated annealing techniques. Presenting a suite of six standard test functions to GA and VFSR codes from previous studies, without any additional fine tuning, strongly suggests that VFSR can be expected to be orders of magnitude more efficient than GA.

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 "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"):
  with members:
    - "Volatility of Volatility of Financial Markets"
    - "STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS"
    - "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"

Connected through hyperedge HE-3 (induced by source paper "When Will a Genetic Algorithm Outperform Hill Climbing?"):
  with members:
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"

Connected through hyperedge HE-4 (induced by source paper "STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS"):
  with members:
    - "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"
    - "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"
    - "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG"

Connected through hyperedge HE-5 (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."
    - "On The State of Evolutionary Computation"
    - "Representation and Evolution of Neural Networks"
    - "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-6 (induced by source paper "Differential Evolution A simple and efficient adaptive scheme for global optimization over continuous spaces"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "On the Usage of Differential Evolution for Function Optimization Differential Evolution (DE) has recently proven"

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