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: "Predictive Control of Opto-Electronic Reconfigurable Interconnection Networks Using Neural Networks"
  Abstract: Opto-electronic reconfigurable interconnection networks are limited by significant control latency when used in large multiprocessor systems. This latency is the time required to analyze the current traffic and reconfigure the network to establish the required paths. The goal of latency hiding is to minimize the effect of this control overhead. In this paper, we introduce a technique that performs latency hiding by learning the patterns of communication traffic and using that information to anticipate the need for communication paths. Hence, the network provides the required communication paths before a request for a path is made. In this study, the communication patterns (memory accesses) of a parallel program are used as input to a time delay neural network (TDNN) to perform online training and prediction. These predicted communication patterns are used by the interconnection network controller that provides routes for the memory requests. Based on our experiments, the neural network was able to learn highly repetitive communication patterns, and was thus able to predict the allocation of communication paths, resulting in a reduction of communication latency. Communication latency is a significant issue in the design of lar ge scale multiprocessor systems. Point-to-point interconnection networks, which directly connect all processors and/or memories, provide minimum communication latency but suffer from high cost and limited scalability. A plethora of electr onic singlestage and multistage networks have been pr oposed, designed and built [Siegel90, Leighton93]. An alternative is the use of opto-electr onic reconfigurable interconnection networks which offer a limited number of high bandwidth communication channels configured on demand, to satisfy the r equired communication traffic [CLMQ94b]. A network controller determines the network configuration based on processor requests. Once the controller provides the optical communication paths requested, the communication proceeds at high speeds. Hence, the end-to-end latency incurred by such networks can be characterized by three components: control time, which is the time needed to determine the new network configuration and to physically establish the paths; launch time, the time to transmit the data into the network; and y time, the time needed for the message to travel through the network to its final destination. For high bandwidth short distance networks, the control time dominates the overall

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"
    - "Routing in Optical Multistage Interconnection Networks: a Neural Network Solution"

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