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: "Graph Coloring with Adaptive Evolutionary Algorithms"
  Abstract: This paper presents the results of an experimental investigation on solving graph coloring problems with Evolutionary Algorithms (EA). After testing different algorithm variants we conclude that the best option is an asexual EA using order-based representation and an adaptation mechanism that periodically changes the fitness function during the evolution. This adaptive EA is general, using no domain specific knowledge, except, of course, from the decoder (fitness function). We compare this adaptive EA to a powerful traditional graph coloring technique DSatur and the Grouping GA on a wide range of problem instances with different size, topology and edge density. The results show that the adaptive EA is superior to the Grouping GA and outperforms DSatur on the hardest problem instances. Furthermore, it scales up better with the problem size than the other two algorithms and indicates a linear computational complexity.

Connected through hyperedge HE-1 (induced by source paper "Solving 3-SAT by GAs Adapting Constraint Weights"):
  with members:
    - "Genetic algorithms with multi-parent recombination"
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"

Connected through hyperedge HE-2 (induced by source paper "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic algorithms with multi-parent recombination"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Multi-parent Recombination"

Connected through hyperedge HE-3 (induced by source paper "Comparison of the SAW-ing Evolutionary Algorithm and the Grouping Genetic Algorithm for Graph Coloring 1"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Genetic algorithms with multi-parent recombination"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"
    - "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "Multi-parent Recombination"
    - "Solving 3-SAT by GAs Adapting Constraint Weights"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

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