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: "Evolution in Time and Space The Parallel Genetic Algorithm"
  Abstract: The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm. Firstly, selection for mating is distributed. Individuals live in a 2-D world. Selection of a mate is done by each individual independently in its neighborhood. Secondly, each individual may improve its fitness during its lifetime by e.g. local hill-climbing. The PGA is totally asynchronous, running with maximal efficiency on MIMD parallel computers. The search strategy of the PGA is based on a small number of active and intelligent individuals, whereas a GA uses a large population of passive individuals. We will investigate the PGA with deceptive problems and the traveling salesman problem. We outline why and when the PGA is succesful. Abstractly, a PGA is a parallel search with information exchange between the individuals. If we represent the optimization problem as a fitness landscape in a certain configuration space, we see, that a PGA tries to jump from two local minima to a third, still better local minima, by using the crossover operator. This jump is (probabilistically) successful, if the fitness landscape has a certain correlation. We show the correlation for the traveling salesman problem by a configuration space analysis. The PGA explores implicitly the above correlation.

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 "A Search Space Analysis of the Job Shop Scheduling Problem"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Staged Hybrid Genetic Search for Seismic Data Imaging"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Evaluating Evolutionary Algorithms"

Connected through hyperedge HE-4 (induced by source paper "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Survey of Evolution Strategies"
    - "Genetic Programming of Minimal Neural Nets Using Occam's Razor"
    - "On the Virtues of Parameterized Uniform Crossover"

Connected through hyperedge HE-5 (induced by source paper "The Schema Theorem and Price's Theorem"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"
    - "Price's Theorem and the MAX Problem"

Connected through hyperedge HE-6 (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"
    - "Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem"
    - "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)"

Connected through hyperedge HE-7 (induced by source paper "A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3CNF Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

Connected through hyperedge HE-8 (induced by source paper "The Role of Development in Genetic Algorithms"):
  with members:
    - "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"

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