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: "Constraining of Weights using Regularities"
  Abstract: In this paper we study how global optimization methods (like genetic algorithms) can be used to train neural networks. We introduce the notion of regularity, for studying properties of the error function that expand the search space in an artificial way. Regularities are used to generate constraints on the weights of the network. In order to find a satisfiable set of constraints we use a constraint logic programming system. Then the training of the network becomes a constrained optimization problem. We also relate the notion of regularity to so-called network transformations.

Connected through hyperedge HE-1 (induced by source paper "Forward-Tracking: A Technique for Searching Beyond Failure"):
  with members:
    - "Evolutionary Training of CLP-Constrained Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Evolutionary Training of CLP-Constrained Neural Networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Forward-Tracking: A Technique for Searching Beyond Failure"

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