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: "Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem"
  Abstract: Genetic algorithms are one example of the use of a random element within an algorithm for combinatorial optimization. We consider the application of the genetic algorithm to a particular problem, the Assembly Line Balancing Problem. A general description of genetic algorithms is given, and their specialized use on our test-bed problems is discussed. We carry out extensive computational testing to find appropriate values for the various parameters associated with this genetic algorithm. These experiments underscore the importance of the correct choice of a scaling parameter and mutation rate to ensure the good performance of a genetic algorithm. We also describe a parallel implementation of the genetic algorithm and give some comparisons between the parallel and serial implementations. Both versions of the algorithm are shown to be effective in producing good solutions for problems of this type (with appropriately chosen parameters).

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 Comparative Study of Genetic Search"
    - "A Survey of Parallel Genetic Algorithms"
    - "Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming"
    - "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 "A Survey of Parallel Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"
    - "Evolution in Time and Space The Parallel Genetic Algorithm"
    - "An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms"
    - "Speeding up Genetic Programming: A Parallel BSP implementation the Bulk Synchronous Parallel Pro gramming (BSP)"

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