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: "Simulated Annealing for Hard Satisfiability Problems"
  Abstract: Satisfiability (SAT) refers to the task of finding a truth assignment that makes an arbitrary boolean expression true. This paper compares a simulated annealing algorithm (SASAT) with GSAT (Selman et al., 1992), a greedy algorithm for solving satisfiability problems. GSAT can solve problem instances that are extremely difficult for traditional satisfiability algorithms. Results suggest that SASAT scales up better as the number of variables increases, solving at least as many hard SAT problems with less effort. The paper then presents an ablation study that helps to explain the relative advantage of SASAT over GSAT. Finally, an improvement to the basic SASAT algorithm is examined, based on a random walk suggested by Selman et al. (1993).

Connected through hyperedge HE-1 (induced by source paper "A NN Algorithm for Boolean Satisfiability Problems"):
  with members:
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"

Connected through hyperedge HE-2 (induced by source paper "Optimal Mutation Rates in Genetic Search"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Coevolution of Mutation Rates"
    - "A Survey of Evolution Strategies"
    - "Between-host evolution of mutation-rate and within-host evolution of virulence."
    - "Mutation Rates as Adaptations"

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