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: "Some Training Subset Selection Methods for Supervised Learning in Genetic Programming"
  Abstract: When using the Genetic Programming (GP) Algorithm on a difficult problem with a large set of training cases, a large population size is needed and a very large number of function-tree evaluations must be carried out. This paper describes how to reduce the number of such evaluations by selecting a small subset of the training data set on which to actually carry out the GP algorithm. Three subset selection methods described in the paper are: Dynamic Subset Selection (DSS), using the current GP run to select `difficult' and/or disused cases, Historical Subset Selection (HSS), using previous GP runs, Random Subset Selection (RSS). GP, GP+DSS, GP+HSS, GP+RSS are compared on a large classification problem. GP+DSS can produce better results in less than 20% of the time taken by GP. GP+HSS can nearly match the results of GP, and, perhaps surprisingly, GP+RSS can occasionally approach the results of GP. GP and GP+DSS are then compared on a smaller problem, and a hybrid Dynamic Fitness Function (DFF), based on DSS, is proposed.

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 "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"):
  with members:
    - "New Methods for Competitive Coevolution"
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"

Connected through hyperedge HE-3 (induced by source paper "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"):
  with members:
    - "New Methods for Competitive Coevolution"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"

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