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: "deeptransport learning spatial temporal dependency for traffic condition forecasting"
  Abstract: Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology. To address the effect attenuation problem, we propose to take account of the traffic of surrounding locations(wider than adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in temporal and spatial domain. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.

Connected through hyperedge HE-1 (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"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"

Connected through hyperedge HE-2 (induced by source paper "demand forecasting from spatiotemporal data with graph networks and temporal guided embedding"):
  with members:
    - "predicting urban dispersal events a two stage framework through deep survival analysis on mobility data"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "taxi demand forecasting a hedge based tessellation strategy for improved accuracy"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "tensor fusion network for multimodal sentiment analysis"
    - "attention is all you need"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"

Connected through hyperedge HE-3 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "self driving cars a survey"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "a behavior driven approach for sampling rare event situations for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "the quo vadis submission at traffic4cast 2019"):
  with members:
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "view synthesis by appearance flow"
    - "dynamic filter networks"
    - "spatial transformer networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-5 (induced by source paper "a spatial temporal decomposition based deep neural network for time series forecasting"):
  with members:
    - "grouped convolutional neural networks for multivariate time series"
    - "mida multiple imputation using denoising autoencoders"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "tensorflow a system for large scale machine learning"
    - "deep convolutional networks on graph structured data"
    - "adam a method for stochastic optimization"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-6 (induced by source paper "mining user behaviour from smartphone data a literature review"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "trajectorynet an embedded gps trajectory representation for point based classification using recurrent neural networks"
    - "smartphone based vehicle telematics a ten year anniversary"
    - "semmatch road semantics based accurate map matching for challenging positioning data"

Connected through hyperedge HE-7 (induced by source paper "revisiting spatial temporal similarity a deep learning framework for traffic prediction"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "xgboost a scalable tree boosting system"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "latent space model for road networks to predict time varying traffic"

Connected through hyperedge HE-8 (induced by source paper "diffusion convolutional recurrent neural network data driven traffic forecasting"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "convolutional sequence to sequence learning"
    - "short term traffic flow forecasting with spatial temporal correlation in a hybrid deep learning framework"
    - "semi supervised classification with graph convolutional networks"
    - "wavenet a generative model for raw audio"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "diffusion convolutional neural 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.AI(Artificial Intelligence)