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: "dynamic spatio temporal graph based cnns for traffic prediction"
  Abstract: Accurate traffic forecast is a challenging problem due to the large-scale problem size, as well as the complex and dynamic nature of spatio-temporal dependency of traffic flow. Most existing graph-based CNNs attempt to capture the static relations while largely neglecting the dynamics underlying sequential data. In this paper, we present dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatio-temporal structures and predict future traffic from historical traffic flow. In particular, DST-GCNN is a two stream network. In the flow prediction stream, we present a novel graph-based spatio-temporal convolutional layer to extract features from a graph representation of traffic flow. Then several such layers are stacked together to predict future traffic over time. Meanwhile, the proximity relations between nodes in the graph are often time variant as the traffic condition changes over time. To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream. Experiments on real traffic datasets demonstrate that the proposed model achieves competitive performances compared with the other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "cdsa cross dimensional self attention for multivariate geo tagged time series imputation"):
  with members:
    - "brits bidirectional recurrent imputation for time series"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "recover missing sensor data with iterative imputing network"
    - "recurrent neural networks for multivariate time series with missing values"
    - "deep residual learning for image recognition"
    - "stacked attention networks for image question answering"

Connected through hyperedge HE-2 (induced by source paper "grids versus graphs partitioning space for improved taxi demand supply forecasts"):
  with members:
    - "recurrent neural networks for time series forecasting"
    - "a comprehensive survey on graph neural networks"
    - "taxi demand supply forecasting impact of spatial partitioning on the performance of neural networks"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "taxi demand forecasting a hedge based tessellation strategy for improved accuracy"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "an aggregating strategy for shifting experts in discrete sequence 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.CV(Computer Vision and Pattern Recognition)