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: "Pruning backpropagation neural networks using modern stochastic optimization techniques"
  Abstract: Approaches combining genetic algorithms and neural networks have received a great deal of attention in recent years. As a result, much work has been reported in two major areas of neural network design: training and topology optimization. This paper focuses on the key issues associated with the problem of pruning a multilayer perceptron using genetic algorithms and simulated annealing. The study presented considers a number of aspects associated with network training that may alter the behavior of a stochastic topology optimizer. Enhancements are discussed that can improve topology searches. Simulation results for the two mentioned stochastic optimization methods applied to nonlinear system identification are presented and compared with a simple random search.

Connected through hyperedge HE-1 (induced by source paper "Extended Selection Mechanisms in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Optimization by Means of Genetic Algorithms"
    - "Genetic Self-Learning"
    - "A Survey of Evolution Strategies"
    - "Self-Adaptation in Genetic Algorithms of external parameters of a GA is seen as a first"

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