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: "Empirical studies of the genetic algorithm with non-coding segments"
  Abstract: The genetic algorithm (GA) is a problem solving method that is modelled after the process of natural selection. We are interested in studying a specific aspect of the GA: the effect of non-coding segments on GA performance. Non-coding segments are segments of bits in an individual that provide no contribution, positive or negative, to the fitness of that individual. Previous research on non-coding segments suggests that including these structures in the GA may improve GA performance. Understanding when and why this improvement occurs will help us to use the GA to its full potential. In this article, we discuss our hypotheses on non-coding segments and describe the results of our experiments. The experiments may be separated into two categories: testing our program on problems from previous related studies, and testing new hypotheses on the effect of non-coding segments.

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:
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "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 "A comparison of the fixed and floating building block representation in the genetic algorithm"):
  with members:
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "Duplication of Coding Segments in Genetic Programming"
    - "A Survey of Intron Research in Genetics"
    - "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."

Connected through hyperedge HE-4 (induced by source paper "A Survey of Intron Research in Genetics"):
  with members:
    - "Complexity Compression and Evolution"
    - "Duplication of Coding Segments in Genetic Programming"
    - "A comparison of the fixed and floating building block representation in the genetic algorithm"
    - "Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code"

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