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: "Evolving Optimal Neural Networks Using Genetic Algorithms with Occam's Razor"
  Abstract: Genetic algorithms have been used for neural networks in two main ways: to optimize the network architecture and to train the weights of a fixed architecture. While most previous work focuses on only one of these two options, this paper investigates an alternative evolutionary approach called Breeder Genetic Programming (BGP) in which the architecture and the weights are optimized simultaneously. The genotype of each network is represented as a tree whose depth and width are dynamically adapted to the particular application by specifically defined genetic operators. The weights are trained by a next-ascent hillclimbing search. A new fitness function is proposed that quantifies the principle of Occam's razor. It makes an optimal trade-off between the error fitting ability and the parsimony of the network. Simulation results on two benchmark problems of differing complexity suggest that the method finds minimal size networks on clean data. The experiments on noisy data show that using Occam's razor not only improves the generalization performance, it also accel erates the convergence speed of evolution. fl Published in Complex Systems, 7(3): 199-220, 1993

Connected through hyperedge HE-1 (induced by source paper "Effects of Occam's Razor in Evolving Sigma-Pi Neural Nets"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Genetic Programming of Minimal Neural Nets Using Occam's Razor"

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