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: "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"
  Abstract: Various extensions to the Genetic Algorithm (GA) attempt to find all or most optima in a search space containing several optima. Many of these emulate natural speciation. For co-evolutionary learning to succeed in a range of management and control problems, such as learning game strategies, such methods must find all or most optima. However, suitable comparison studies are rare. We compare two similar GA specia-tion methods, fitness sharing and implicit sharing. Using a realistic letter classification problem, we find they have advantages under different circumstances. Implicit sharing covers optima more comprehensively, when the population is large enough for a species to form at each optimum. With a population not large enough to do this, fitness sharing can find the optima with larger basins of attraction, and ignore the peaks with narrow bases, while implicit sharing is more easily distracted. This indicates that for a speciated GA trying to find as many near-global optima as possible, implicit sharing works well only if the population is large enough. This requires prior knowledge of how many peaks exist.

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 "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Automatic Modularization by Speciation"
    - "Evolving Complex Structures via Co- operative Coevolution"
    - "Self-Nonself Discrimination in a Computer"

Connected through hyperedge HE-3 (induced by source paper "New Methods for Competitive Coevolution"):
  with members:
    - "A Competitive Approach to Game Learning"
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"
    - "17 Massively Parallel Genetic Programming"
    - "Automatic Modularization by Speciation"
    - "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"
    - "Dynamics of Co-evolutionary Learning"

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