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: "bike flow prediction with multi graph convolutional networks"
  Abstract: One fundamental issue in managing bike sharing systems is the bike flow prediction. Due to the hardness of predicting the flow for a single station, recent research works often predict the bike flow at cluster-level. While such studies gain satisfactory prediction accuracy, they cannot directly guide some fine-grained bike sharing system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy leveraging the breakthroughs of deep learning techniques. We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective. More specifically, we construct multiple inter-station graphs for a bike sharing system. In each graph, nodes are stations, and edges are a certain type of relations between stations. Then, multiple graphs are constructed to reflect heterogeneous relationships (e.g., distance, ride record correlation). Afterward, we fuse the multiple graphs and then apply the convolutional layers on the fused graph to predict station-level future bike flow. In addition to the estimated bike flow value, our model also gives the prediction confidence interval so as to help the bike sharing system managers make decisions. Using New York City and Chicago bike sharing data for experiments, our model can outperform state-of-the-art station-level prediction models by reducing 25.1% and 17.0% of prediction error in New York City and Chicago, respectively.

Connected through hyperedge HE-1 (induced by source paper "multi modal graph interaction for multi graph convolution network in urban spatiotemporal forecasting"):
  with members:
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "multimodal machine learning a survey and taxonomy"
    - "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"
    - "a survey on multi task learning"

Connected through hyperedge HE-2 (induced by source paper "urban flows prediction from spatial temporal data using machine learning a survey"):
  with members:
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "spatio temporal data mining a survey of problems and methods"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "predicting citywide crowd flows using deep spatio temporal residual networks"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "dynamic demand prediction for expanding electric vehicle sharing systems a graph sequence learning approach"):
  with members:
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "semi supervised classification with graph convolutional networks"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-4 (induced by source paper "demand prediction for electric vehicle sharing"):
  with members:
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "semi supervised classification with graph convolutional networks"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-5 (induced by source paper "creditprint credit investigation via geographic footprints by deep learning"):
  with members:
    - "attention is all you need"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

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)