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: "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
  Abstract: Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting has not yet fully been exploited in terms of the depth of the model architecture, the spatial scale of the prediction area, and the predictive power of spatial-temporal data. In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU- LSTM) neural network architecture is proposed, which considers both forward and backward dependencies in time series data, to predict network-wide traffic speed. A bidirectional LSTM (BDLSM) layer is exploited to capture spatial features and bidirectional temporal dependencies from historical data. To the best of our knowledge, this is the first time that BDLSTMs have been applied as building blocks for a deep architecture model to measure the backward dependency of traffic data for prediction. The proposed model can handle missing values in input data by using a masking mechanism. Further, this scalable model can predict traffic speed for both freeway and complex urban traffic networks. Comparisons with other classical and state-of-the-art models indicate that the proposed SBU-LSTM neural network achieves superior prediction performance for the whole traffic network in both accuracy and robustness.

Connected through hyperedge HE-1 (induced by source paper "interpretable crowd flow prediction with spatial temporal self attention"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "deep convolutional networks on graph structured data"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-2 (induced by source paper "constructing a highlight classifier with an attention based lstm neural network"):
  with members:
    - "personalized neural embeddings for collaborative filtering with text"
    - "deep learning for sentiment analysis a survey"
    - "generalizing word embeddings using bag of subwords"
    - "word mover s embedding from word2vec to document embedding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automatic music highlight extraction using convolutional recurrent attention networks"
    - "attention is all you need"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-3 (induced by source paper "stgrat a spatio temporal graph attention network for traffic forecasting"):
  with members:
    - "graph wavenet for deep spatial temporal graph modeling"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-4 (induced by source paper "graph partitioning based diffusion convolution recurrent neural network for large scale traffic forecasting"):
  with members:
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "multi task learning as multi objective optimization"
    - "character level language modeling with deeper self attention"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "cascade r cnn delving into high quality object detection"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-5 (induced by source paper "spatial temporal self attention network for flow prediction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "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"
    - "short term traffic flow forecasting with spatial temporal correlation in a hybrid deep learning framework"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-6 (induced by source paper "two stream multi channel convolutional neural network tm cnn for multi lane traffic speed prediction considering traffic volume impact"):
  with members:
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "multistep speed prediction on traffic networks a graph convolutional sequence to sequence learning approach with attention mechanism"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"

Connected through hyperedge HE-7 (induced by source paper "d gan deep generative adversarial nets for spatio temporal prediction"):
  with members:
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "unsupervised learning of disentangled representations from video"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-8 (induced by source paper "time aware gated recurrent unit networks for road surface friction prediction using historical data"):
  with members:
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "road friction estimation for connected vehicles using supervised machine learning"
    - "recurrent neural networks for multivariate time series with missing values"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)