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: "A Comparative Study of Genetic Search"
  Abstract: We present a comparative study of genetic algorithms and their search properties when treated as a combinatorial optimization technique. This is done in the context of the NP-hard problem MAX-SAT, the comparison being relative to the Metropolis process, and by extension, simulated annealing. Our contribution is two-fold. First, we show that for large and difficult MAX-SAT instances, the contribution of cross-over to the search process is marginal. Little is lost if it is dispensed altogether, running mutation and selection as an enlarged Metropolis process. Second, we show that for these problem instances, genetic search consistently performs worse than simulated annealing when subject to similar resource bounds. The correspondence between the two algorithms is made more precise via a decomposition argument, and provides a framework for interpreting our results.

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 "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Surgery"
    - "A Survey of Parallel Genetic Algorithms"
    - "Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming"
    - "Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem"
    - "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"
    - "On the Virtues of Parameterized Uniform Crossover"
    - "As mentioned in the introduction, an encod-ing/crossover pair makes a spectrum of geographical linkages. A"

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"
    - "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"
    - "A Generalized Permutation Approach to Job Shop Scheduling with Genetic Algorithms"

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