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: "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
  Abstract: Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

Connected through hyperedge HE-1 (induced by source paper "a neural architecture for detecting confusion in eye tracking data"):
  with members:
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "appearance based gaze estimation in the wild"
    - "imagenet large scale visual recognition challenge"
    - "deep visual semantic alignments for generating image descriptions"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "deep learning for spatio temporal data mining a survey"):
  with members:
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "a comprehensive survey on graph neural networks"
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "where to go next a spatio temporal lstm model for next poi recommendation"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "deep sequence learning with auxiliary information for traffic prediction"

Connected through hyperedge HE-3 (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"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "multi range attentive bicomponent graph convolutional network for traffic forecasting"):
  with members:
    - "graph wavenet for deep spatial temporal graph modeling"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "neural message passing for quantum chemistry"
    - "predicting citywide crowd flows using deep spatio temporal residual networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "event ticket price prediction with deep neural network on spatial temporal sparse data"):
  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"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "machine learning modeling for time series problem predicting flight ticket prices"
    - "xgboost a scalable tree boosting system"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "online real time multiple spatiotemporal action localisation and prediction"

Connected through hyperedge HE-6 (induced by source paper "graph markov network for traffic forecasting with missing data"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "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"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "recurrent neural networks for multivariate time series with missing values"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "the quo vadis submission at traffic4cast 2019"):
  with members:
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "deeptransport learning spatial temporal dependency for traffic condition forecasting"
    - "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-8 (induced by source paper "two stream multi channel convolutional neural network tm cnn for multi lane traffic speed prediction considering traffic volume impact"):
  with members:
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "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"
    - "multistep speed prediction on traffic networks a graph convolutional sequence to sequence learning approach with attention mechanism"

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)