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: "st unet a spatio temporal u network for graph structured time series modeling"
  Abstract: The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is still a lack of effective means to extract dynamic complex features from spatio-temporal structures. Particularly, conventional models such as convolutional networks or recurrent neural networks are incapable of revealing the temporal patterns in short or long terms and exploring the spatial properties in local or global scope from spatio-temporal graphs simultaneously. To tackle this problem, we design a novel multi-scale architecture, Spatio-Temporal U-Net (ST-UNet), for graph-structured time series modeling. In this U-shaped network, a paired sampling operation is proposed in spacetime domain accordingly: the pooling (ST-Pool) coarsens the input graph in spatial from its deterministic partition while abstracts multi-resolution temporal dependencies through dilated recurrent skip connections; based on previous settings in the downsampling, the unpooling (ST-Unpool) restores the original structure of spatio-temporal graphs and resumes regular intervals within graph sequences. Experiments on spatio-temporal prediction tasks demonstrate that our model effectively captures comprehensive features in multiple scales and achieves substantial improvements over mainstream methods on several real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "ddp gcn multi graph convolutional network for spatiotemporal traffic forecasting"):
  with members:
    - "multi modal graph interaction for multi graph convolution network in urban spatiotemporal forecasting"
    - "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"
    - "semi supervised classification with graph convolutional 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-2 (induced by source paper "graph representation learning via hard and channel wise attention networks"):
  with members:
    - "learning visual question answering by bootstrapping hard attention"
    - "hierarchical graph representation learning with differentiable pooling"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "large scale learnable graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "non local neural networks"
    - "attention is all you need"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "graph convolutional modules for traffic forecasting"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "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"
    - "semi supervised classification with graph convolutional networks"
    - "spectral networks and locally connected networks on graphs"

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)