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 hybrid neurowavelet predictor for qos control and stability"
  Abstract: For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use wavelet analysis, providing compression and denoising on the observed time series of the amount of past user requests; and a recurrent neural network trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations.

Connected through hyperedge HE-1 (induced by source paper "an agent driven semantical identifier using radial basis neural networks and reinforcement learning"):
  with members:
    - "a cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in openmp"
    - "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"
    - "infinite hidden relational models"

Connected through hyperedge HE-2 (induced by source paper "simplified firefly algorithm for 2d image key points search"):
  with members:
    - "a cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in openmp"
    - "using modularity metrics to assist move method refactoring of large systems"
    - "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 cascade neural network architecture investigating surface plasmon polaritons propagation for thin metals in openmp"
    - "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)