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 dual stage attention based recurrent neural network for time series prediction"
  Abstract: The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a.k.a., input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction.

Connected through hyperedge HE-1 (induced by source paper "conditional mutual information based contrastive loss for financial time series forecasting"):
  with members:
    - "on mutual information maximization for representation learning"
    - "understanding the limitations of variational mutual information estimators"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "ccmi classifier based conditional mutual information estimation"
    - "attention is all you need"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "temporal tensor transformation network for multivariate time series prediction"):
  with members:
    - "wavenet a generative model for raw audio"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "latent variable algorithms for multimodal learning and sensor fusion"):
  with members:
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"
    - "improved speech reconstruction from silent video"
    - "attention is all you need"
    - "visual speech enhancement"
    - "seeing through noise visually driven speaker separation and enhancement"
    - "lipnet end to end sentence level lipreading"

Connected through hyperedge HE-5 (induced by source paper "ddlstm dual domain lstm for cross dataset action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "autodial automatic domain alignment layers"
    - "lstm a search space odyssey"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep hashing network for unsupervised domain adaptation"
    - "cascaded boundary regression for temporal action detection"

Connected through hyperedge HE-6 (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"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "forecasting across time series databases using recurrent neural networks on groups of similar series a clustering approach"

Connected through hyperedge HE-7 (induced by source paper "joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values"):
  with members:
    - "megan a generative adversarial network for multi view network embedding"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "joint modeling of dense and incomplete trajectories for citywide traffic volume inference"
    - "brits bidirectional recurrent imputation for time series"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "a memory network based solution for multivariate time series forecasting"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "end to end learning for short text expansion"

Connected through hyperedge HE-8 (induced by source paper "mgp atttcn an interpretable machine learning model for the prediction of sepsis"):
  with members:
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "medical time series classification with hierarchical attention based temporal convolutional networks a case study of myotonic dystrophy diagnosis"
    - "model based reinforcement learning for sepsis treatment"
    - "mattnet modular attention network for referring expression comprehension"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "som vae interpretable discrete representation learning on time series"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"

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)