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: "latent space model for road networks to predict time varying traffic"
  Abstract: Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamics associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learn the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly with given data. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the utility superiority of our framework for real-time traffic prediction on large road networks over competitors as well as a baseline graph-based LSM.

Connected through hyperedge HE-1 (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-2 (induced by source paper "dynamic spatial temporal representation leaning for crowd flow prediction"):
  with members:
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "crowd counting using deep recurrent spatial aware network"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "attentive crowd flow machines"
    - "crowd flow prediction by deep spatio temporal transfer learning"
    - "visual analytics of movement pattern based on time spatial data a neural net approach"

Connected through hyperedge HE-3 (induced by source paper "contextualized spatial temporal network for taxi origin destination demand prediction"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "contrast oriented deep neural networks for salient object detection"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "facial landmark machines a backbone branches architecture with progressive representation learning"
    - "non local neural networks"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "a long short term memory recurrent neural network framework for network traffic matrix prediction"
    - "disc deep image saliency computing via progressive representation learning"

Connected through hyperedge HE-4 (induced by source paper "how do urban incidents affect traffic speed a deep graph convolutional network for incident driven traffic speed prediction"):
  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"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-5 (induced by source paper "position aware convolutional networks for traffic prediction"):
  with members:
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "xgboost a scalable tree boosting system"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-6 (induced by source paper "spatiotemporal tile based attention guided lstms for traffic video prediction"):
  with members:
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "potential passenger flow prediction a novel study for urban transportation development"):
  with members:
    - "sample adaptive multiple kernel learning for failure prediction of railway points"
    - "a survey on multi view learning"

Connected through hyperedge HE-8 (induced by source paper "attentive crowd flow machines"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "spatiotemporal modeling for crowd counting in videos"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attention aware face hallucination via deep reinforcement learning"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "deeptrend a deep hierarchical neural network for traffic flow 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.SI(Social and Information Networks)