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: "neural decomposition of time series data for effective generalization"
  Abstract: We present a neural network technique for the analysis and extrapolation of time-series data called  neural decomposition (ND) . Units with a sinusoidal activation function are used to perform a Fourier-like decomposition of training samples into a sum of sinusoids, augmented by units with nonperiodic activation functions to capture linear trends and other nonperiodic components. We show how careful weight initialization can be combined with regularization to form a simple model that generalizes well. Our method generalizes effectively on the Mackey–Glass series, a data set of unemployment rates as reported by the U.S. Department of Labor Statistics, a time-series of monthly international airline passengers, the monthly ozone concentration in downtown Los Angeles, and an unevenly sampled time series of oxygen isotope measurements from a cave in north India. We find that ND outperforms popular time-series forecasting techniques, including long short-term memory network, echo-state networks, autoregressive integrated moving average (ARIMA), seasonal ARIMA, support vector regression with a radial basis function, and Gashler and Ashmore’s model.

Connected through hyperedge HE-1 (induced by source paper "time2vec learning a vector representation of time"):
  with members:
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "learning temporal point processes via reinforcement learning"
    - "can recurrent neural networks warp time"
    - "deep reinforcement learning of marked temporal point processes"
    - "streaming graph neural networks"
    - "neural ordinary differential equations"
    - "learning dynamic embeddings from temporal interactions"

Connected through hyperedge HE-2 (induced by source paper "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks"):
  with members:
    - "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"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "fourier rnns for sequence prediction"):
  with members:
    - "complex gated recurrent neural networks"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "gated orthogonal recurrent units on learning to forget"
    - "deep complex networks"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "hierarchical multiscale recurrent neural networks"
    - "comparing time and frequency domain for audio event recognition using deep learning"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "fourier rnns for sequence analysis and prediction"):
  with members:
    - "complex gated recurrent neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "deep complex networks"
    - "comparing time and frequency domain for audio event recognition using deep learning"
    - "unitary evolution recurrent neural networks"
    - "a clockwork rnn"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-5 (induced by source paper "traffic prediction based on random connectivity in deep learning with long short term memory"):
  with members:
    - "stochasticnet forming deep neural networks via stochastic connectivity"

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)