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: "short term traffic flow forecasting with spatial temporal correlation in a hybrid deep learning framework"
  Abstract: Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks. And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can successfully characterize the temporal correlation, thus exhibits superior capability for time series tasks. Traffic flow data have plentiful characteristics on both time and space domain. However, applications of CNN and LSTM approaches on traffic flow are limited. In this paper, we propose a novel deep architecture combined CNN and LSTM to forecast future traffic flow (CLTFP). An 1-dimension CNN is exploited to capture spatial features of traffic flow, and two LSTMs are utilized to mine the short-term variability and periodicities of traffic flow. Given those meaningful features, the feature-level fusion is performed to achieve short-term forecasting. The proposed CLTFP is compared with other popular forecasting methods on an open datasets. Experimental results indicate that the CLTFP has considerable advantages in traffic flow forecasting. in additional, the proposed CLTFP is analyzed from the view of Granger Causality, and several interesting properties of CLTFP are discovered and discussed .

Connected through hyperedge HE-1 (induced by source paper "spatial temporal self attention network for flow prediction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-2 (induced by source paper "t gcn a temporal graph convolutional network for traffic prediction"):
  with members:
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-3 (induced by source paper "3d graph convolutional networks with temporal graphs a spatial information free framework for traffic forecasting"):
  with members:
    - "learning convolutional neural networks for graphs"
    - "tensorflow a system for large scale machine learning"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "sequence to sequence learning with neural networks"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-4 (induced by source paper "mapping road safety features from streetview imagery a deep learning approach"):
  with members:
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-5 (induced by source paper "dynamic spatio temporal graph based cnns for traffic prediction"):
  with members:
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "deep sequence learning with auxiliary information for traffic prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deeptrend a deep hierarchical neural network for traffic flow prediction"
    - "ssd single shot multibox detector"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"):
  with members:
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "convolutional sequence to sequence learning"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"):
  with members:
    - "lstm a search space odyssey"
    - "a critical review of recurrent neural networks for sequence learning"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "show and tell a neural image caption generator"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "diffusion convolutional recurrent neural network data driven traffic forecasting"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "convolutional sequence to sequence learning"
    - "deeptransport learning spatial temporal dependency for traffic condition forecasting"
    - "semi supervised classification with graph convolutional networks"
    - "wavenet a generative model for raw audio"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "diffusion convolutional 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.CV(Computer Vision and Pattern Recognition)