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: "r2n2 residual recurrent neural networks for multivariate time series forecasting"
  Abstract: Multivariate time-series modeling and forecasting is an important problem with numerous applications. Traditional approaches such as VAR (vector auto-regressive) models and more recent approaches such as RNNs (recurrent neural networks) are indispensable tools in modeling time-series data. In many multivariate time series modeling problems, there is usually a significant linear dependency component, for which VARs are suitable, and a nonlinear component, for which RNNs are suitable. Modeling such times series with only VAR or only RNNs can lead to poor predictive performance or complex models with large training times. In this work, we propose a hybrid model called R2N2 (Residual RNN), which first models the time series with a simple linear model (like VAR) and then models its residual errors using RNNs. R2N2s can be trained using existing algorithms for VARs and RNNs. Through an extensive empirical evaluation on two real world datasets (aviation and climate domains), we show that R2N2 is competitive, usually better than VAR or RNN, used alone. We also show that R2N2 is faster to train as compared to an RNN, while requiring less number of hidden units.

Connected through hyperedge HE-1 (induced by source paper "streaming adaptation of deep forecasting models using adaptive recurrent units"):
  with members:
    - "fast parametric learning with activation memorization"
    - "armdn associative and recurrent mixture density networks for eretail demand forecasting"
    - "learning long term dependencies via fourier recurrent units"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "the statistical recurrent unit"
    - "labeled memory networks for online model adaptation"
    - "a simple neural attentive meta learner"

Connected through hyperedge HE-2 (induced by source paper "seq2graph discovering dynamic dependencies from multivariate time series with multi level attention"):
  with members:
    - "recent advances in recurrent neural networks"
    - "scalable and accurate deep learning for electronic health records"
    - "attention is all you need"
    - "multi period time series modeling with sparsity via bayesian variational inference"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "foundations of sequence to sequence modeling for time series"):
  with members:
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "long term forecasting using higher order tensor rnns"
    - "norm based capacity control in neural networks"
    - "sequence to sequence learning with 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)