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: "Using Problem Generators to Explore the Effects of Epistasis"
  Abstract: In this paper we develop an empirical methodology for studying the behavior of evolutionary algorithms based on problem generators. We then describe three generators that can be used to study the effects of epistasis on the performance of EAs. Finally, we illustrate the use of these ideas in a preliminary exploration of the effects of epistasis on simple GAs.

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:
    - "On The State of Evolutionary Computation"
    - "Adapting Crossover in a Genetic Algorithm"
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"
    - "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 "On the Effectiveness of Evolutionary Search in High-Dimensional NK-Landscapes"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"
    - "A Genetic Local Search Approach to the Quadratic Assignment Problem"

Connected through hyperedge HE-3 (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-4 (induced by source paper "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "HGA: A Hardware-Based Genetic Algorithm"
    - "Vector Quantizer Design Using Genetic Algorithms"
    - "A Comparative Study of Genetic Search"
    - "How good are genetic algorithms at finding large cliques: an experimental study"
    - "An Evolutionary Approach to Vector Quantizer Design"
    - "Using Genetic Algorithms for Supervised Concept Learning"
    - "A Generalized Permutation Approach to Job Shop Scheduling with 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