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: "Routing in Optical Multistage Interconnection Networks: a Neural Network Solution"
  Abstract: There has been much interest in using optics to implement computer interconnection networks. However, there has been little discussion of any routing methodologies besides those already used in electronics. In this paper, a neural network routing methodology is proposed that can generate control bits for a broad range of optical multistage interconnection networks (OMINs). Though we present no optical implementation of this methodology, we illustrate its control for an optical interconnection network. These OMINs can be used as communication media for distributed computing systems. The routing methodology makes use of an Artificial Neural Network (ANN) that functions as a parallel computer for generating the routes. The neural network routing scheme can be applied to electrical as well as optical interconnection networks. However, since the ANN can be implemented using optics, this routing approach is especially appealing for an optical computing environment. Although the ANN does not always generate the best solution, the parallel nature of the ANN computation may make this routing scheme faster than conventional routing approaches, especially for OMINs that have an irregular structure. Furthermore, the ANN router is fault-tolerant. Results are shown for generating routes in a 16 fi 16, 3-stage OMIN.

Connected through hyperedge HE-1 (induced by source paper "Performance of On-Line Learning Methods in Predicting Multiprocessor Memory Access Patterns"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"
    - "Predictive Control of Opto-Electronic Reconfigurable Interconnection Networks Using 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: Neural_Networks