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 Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
  Abstract: This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.

Connected through hyperedge HE-1 (induced by source paper "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem"
    - "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"
    - "Voting for Schemata"
    - "A comparison of the fixed and floating building block representation in the genetic algorithm"
    - "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."
    - "The Impact of External Dependency in Genetic Programming Primitives"

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 "Automatic Modularization by Speciation"):
  with members:
    - "New Methods for Competitive Coevolution"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

Connected through hyperedge HE-4 (induced by source paper "Self-Nonself Discrimination in a Computer"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "New Methods for Competitive Coevolution"

Connected through hyperedge HE-6 (induced by source paper "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"):
  with members:
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "A Cooperative Coevolutionary Approach to Function Optimization"

Connected through hyperedge HE-7 (induced by source paper "Evolving Complex Structures via Co- operative Coevolution"):
  with members:
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "A Cooperative Coevolutionary Approach to Function Optimization"

Connected through hyperedge HE-8 (induced by source paper "A Coevolutionary Approach to Learning Sequential Decision Rules"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Knowledge-Based Genetic Learning"
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Automatic Modularization by Speciation"
    - "Evolving Complex Structures via Co- operative Coevolution"

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