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: "training deep fourier neural networks to fit time series data"
  Abstract: We present a method for training a deep neural network containing sinusoidal activation functions to fit to time-series data. Weights are initialized using a fast Fourier transform, then trained with regularization to improve generalization. A simple dynamic parameter tuning method is employed to adjust both the learning rate and regularization term, such that stability and efficient training are both achieved. We show how deeper layers can be utilized to model the observed sequence using a sparser set of sinusoid units, and how non-uniform regularization can improve generalization by promoting the shifting of weight toward simpler units. The method is demonstrated with time-series problems to show that it leads to effective extrapolation of nonlinear trends.

Connected through hyperedge HE-1 (induced by source paper "goldilocks neural networks"):
  with members:
    - "improving deep learning by inverse square root linear units isrlus"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "a continuum among logarithmic linear and exponential functions and its potential to improve generalization in neural networks"
    - "deep learning with s shaped rectified linear activation units"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks"):
  with members:
    - "neural decomposition of time series data for effective generalization"
    - "synthesizing neural network controllers with probabilistic model based reinforcement learning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "one shot visual imitation learning via meta learning"
    - "an overview of multi task learning in deep neural networks"
    - "proximal policy optimization algorithms"
    - "learning to reinforcement learn"
    - "understanding deep learning requires rethinking generalization"

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)