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: "Solving 3-SAT by GAs Adapting Constraint Weights"
  Abstract: Handling NP complete problems with GAs is a great challenge. In particular the presence of constraints makes finding solutions hard for a GA. In this paper we present a problem independent constraint handling mechanism, Stepwise Adaptation of Weights (SAW), and apply it for solving the 3-SAT problem. Our experiments prove that the SAW mechanism substantially increases GA performance. Furthermore, we compare our SAW-ing GA with the best heuristic technique we could trace, WGSAT, and conclude that the GA is superior to the heuristic method.

Connected through hyperedge HE-1 (induced by source paper "Graph Coloring with Adaptive Evolutionary Algorithms"):
  with members:
    - "Genetic algorithms with multi-parent recombination"
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "Comparison of the SAW-ing Evolutionary Algorithm and the Grouping Genetic Algorithm for Graph Coloring 1"

Connected through hyperedge HE-2 (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"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

Connected through hyperedge HE-3 (induced by source paper "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "HGA: A Hardware-Based Genetic Algorithm"
    - "Vector Quantizer Design Using Genetic Algorithms"
    - "A Comparative Study of Genetic Search"
    - "Using Problem Generators to Explore the Effects of Epistasis"
    - "How good are genetic algorithms at finding large cliques: an experimental study"
    - "An Evolutionary Approach to Vector Quantizer Design"
    - "Using Genetic Algorithms for Supervised Concept 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