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: "deeptrend a deep hierarchical neural network for traffic flow prediction"
  Abstract: In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend, a deep hierarchical neural network used for traffic flow prediction which considers and extracts the time-variant trend. DeepTrend has two stacked layers: extraction layer and prediction layer. Extraction layer, a fully connected layer, is used to extract the time-variant trend in traffic flow by feeding the original flow series concatenated with corresponding simple average trend series. Prediction layer, an LSTM layer, is used to make flow prediction by feeding the obtained trend from the output of extraction layer and calculated residual series. To make the model more effective, DeepTrend needs first pre-trained layer-by-layer and then fine-tuned in the entire network. Experiments show that DeepTrend can noticeably boost the prediction performance compared with some traditional prediction models and LSTM with detrending based methods.

Connected through hyperedge HE-1 (induced by source paper "attentive crowd flow machines"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "spatiotemporal modeling for crowd counting in videos"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attention aware face hallucination via deep reinforcement learning"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "scalable deep traffic flow neural networks for urban traffic congestion prediction"

Connected through hyperedge HE-2 (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"
    - "short term traffic flow forecasting with spatial temporal correlation in a hybrid deep learning framework"
    - "ssd single shot multibox detector"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

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)