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: "position based content attention for time series forecasting with sequence to sequence rnns"
  Abstract: We propose here an extended attention model for sequence-to-sequence recurrent neural networks (RNNs) designed to capture (pseudo-)periods in time series. This extended attention model can be deployed on top of any RNN and is shown to yield state-of-the-art performance for time series forecasting on several univariate and multivariate time series.

Connected through hyperedge HE-1 (induced by source paper "convolution attention and structure embedding"):
  with members:
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "deep biaffine attention for neural dependency parsing"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "xception deep learning with depthwise separable convolutions"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-2 (induced by source paper "exploring interpretable lstm neural networks over multi variable data"):
  with members:
    - "fine grained attention mechanism for neural machine translation"
    - "an interpretable lstm neural network for autoregressive exogenous model"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "deep multi output forecasting learning to accurately predict blood glucose trajectories"
    - "methods for interpreting and understanding deep neural networks"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "beyond word importance contextual decomposition to extract interactions from lstms"

Connected through hyperedge HE-3 (induced by source paper "recurrent neural networks for time series forecasting current status and future directions"):
  with members:
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "sales demand forecast in e commerce using a long short term memory neural network methodology"
    - "gluonts probabilistic time series models in python"
    - "temporal pattern attention for multivariate time series forecasting"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"

Connected through hyperedge HE-4 (induced by source paper "convolution is outer product"):
  with members:
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-5 (induced by source paper "temporal attention augmented bilinear network for financial time series data analysis"):
  with members:
    - "benchmark dataset for mid price forecasting of limit order book data with machine learning methods"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "learning deep matrix representations"
    - "lstm a search space odyssey"
    - "benchmark dataset for mid price prediction of limit order book data"
    - "multilinear class specific discriminant analysis"
    - "tensor representation in high frequency financial data for price change prediction"
    - "matrix neural networks"

Connected through hyperedge HE-6 (induced by source paper "multi period time series modeling with sparsity via bayesian variational inference"):
  with members:
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "a recurrent latent variable model for sequential data"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "generating sequences with recurrent neural networks"

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.LG(Machine Learning)