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: "Dynamic Parameter Encoding for Genetic Algorithms"
  Abstract: The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA population to adaptively control the mapping from fixed-length binary genes to real values. DPE is shown to be empirically effective and amenable to analysis; we explore the problem of premature convergence in GAs through two convergence models.

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 "Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques"):
  with members:
    - "Empirical studies of the genetic algorithm with non-coding segments"
    - "Basic PSugal an extension package for the development of Distributed Genetic Algorithms"
    - "Soft Computing: the Convergence of Emerging Reasoning Technologies"

Connected through hyperedge HE-3 (induced by source paper "Representation and Evolution of Neural Networks"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "LEARNING BY ERROR-DRIVEN DECOMPOSITION"
    - "Feature selection through Functional Links with Evolutionary Computation for Neural Networks"
    - "Genetic Encoding Strategies for Neural Networks"

Connected through hyperedge HE-4 (induced by source paper "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"):
  with members:
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Evolving Non-Determinism: An Inventive and Efficient Tool 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 "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"):
  with members:
    - "A Stochastic Search Approach to Grammar Induction"
    - "A Survey of Evolution Strategies"
    - "Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies"
    - "Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies"
    - "Evolving Sorting Networks using Genetic Programming and Rapidly Reconfigurable Field-Programmable Gate Arrays Convergent Design, L.L.C."
    - "Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate"

Connected through hyperedge HE-6 (induced by source paper "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Differential Evolution A simple and efficient adaptive scheme for global optimization over continuous spaces"
    - "When Will a Genetic Algorithm Outperform Hill Climbing?"
    - "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"
    - "STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS"

Connected through hyperedge HE-7 (induced by source paper "On The State of Evolutionary Computation"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Adapting Crossover in a Genetic Algorithm"
    - "A Survey of Evolution Strategies"
    - "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"

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