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: "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
  Abstract: Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

Connected through hyperedge HE-1 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction"):
  with members:
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning semantic specific graph representation for multi label image recognition"
    - "graph convolutional neural networks for web scale recommender systems"
    - "graph to sequence learning using gated graph neural networks"
    - "hybrid knowledge routed modules for large scale object detection"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"

Connected through hyperedge HE-2 (induced by source paper "graph inference learning for semi supervised classification"):
  with members:
    - "gaussian induced convolution for graphs"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "topology adaptive graph convolutional networks"
    - "learning to learn meta critic networks for sample efficient learning"
    - "shift aggregate extract networks"
    - "scene graph generation by iterative message passing"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-3 (induced by source paper "interpretable crowd flow prediction with spatial temporal self attention"):
  with members:
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "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-4 (induced by source paper "cmetric a driving behavior measure using centrality functions"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "graphrqi classifying driver behaviors using graph spectrums"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "identifying driver behaviors using trajectory features for vehicle navigation"

Connected through hyperedge HE-5 (induced by source paper "block hankel tensor arima for multiple short time series forecasting"):
  with members:
    - "manifold modeling in embedded space a perspective for interpreting deep image prior"
    - "model agnostic time series analysis via matrix estimation"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "long term forecasting using higher order tensor rnns"
    - "xgboost a scalable tree boosting system"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"

Connected through hyperedge HE-6 (induced by source paper "dynamic spatiotemporal graph neural network with tensor network"):
  with members:
    - "graph wavenet for deep spatial temporal graph modeling"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "wavenet a generative model for raw audio"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"

Connected through hyperedge HE-7 (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"
    - "st unet a spatio temporal u network for graph structured time series modeling"
    - "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-8 (induced by source paper "inference for network structure and dynamics from time series data via graph neural network"):
  with members:
    - "generative graph convolutional network for growing graphs"
    - "deepnc deep generative network completion"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "community detection in partially observable social networks"

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)