human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in openmp"
  Abstract: Surface plasmon polaritons (SPPs) confined along metal-dielectric interface have attracted a relevant interest in the area of ultracompact photonic circuits, photovoltaic devices and other applications due to their strong field confinement and enhancement. This paper investigates a novel cascade neural network (NN) architecture to find the dependance of metal thickness on the SPP propagation. Additionally, a novel training procedure for the proposed cascade NN has been developed using an OpenMP-based framework, thus greatly reducing training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand.

Connected through hyperedge HE-1 (induced by source paper "an agent driven semantical identifier using radial basis neural networks and reinforcement learning"):
  with members:
    - "simplified firefly algorithm for 2d image key points search"
    - "improving files availability for bittorrent using a diffusion model"
    - "using modularity metrics to assist move method refactoring of large systems"
    - "a hybrid neurowavelet predictor for qos control and stability"
    - "infinite hidden relational models"

Connected through hyperedge HE-2 (induced by source paper "simplified firefly algorithm for 2d image key points search"):
  with members:
    - "using modularity metrics to assist move method refactoring of large systems"
    - "a hybrid neurowavelet predictor for qos control and stability"
    - "analysis of a nature inspired firefly algorithm based back propagation neural network training"

Connected through hyperedge HE-3 (induced by source paper "improving files availability for bittorrent using a diffusion model"):
  with members:
    - "a hybrid neurowavelet predictor for qos control and stability"
    - "innovative second generation wavelets construction with recurrent neural networks for solar radiation forecasting"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.NE(Neural and Evolutionary Computing)