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: "When Will a Genetic Algorithm Outperform Hill Climbing?"
  Abstract: We analyze a simple hill-climbing algorithm (RMHC) that was previously shown to outperform a genetic algorithm (GA) on a simple "Royal Road" function. We then analyze an "idealized" genetic algorithm (IGA) that is significantly faster than RMHC and that gives a lower bound for GA speed. We identify the features of the IGA that give rise to this speedup, and discuss how these features can be incorporated into a real GA.

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"
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "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."

Connected through hyperedge HE-2 (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."
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "Differential Evolution A simple and efficient adaptive scheme for global optimization over continuous spaces"
    - "%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-3 (induced by source paper "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"):
  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"
    - "The Schema Theorem and Price's Theorem"

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