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: "deep multi view spatial temporal network for taxi demand prediction"
  Abstract: Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from large-scale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "revisiting convolutional neural networks for urban flow analytics"):
  with members:
    - "fine grained urban flow inference"
    - "deepdpm dynamic population mapping via deep neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "pyramid scene parsing network"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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 "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"
    - "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"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"

Connected through hyperedge HE-4 (induced by source paper "multi modal graph interaction for multi graph convolution network in urban spatiotemporal forecasting"):
  with members:
    - "bike flow prediction with multi graph convolutional networks"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "multimodal machine learning a survey and taxonomy"
    - "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"
    - "a survey on multi task learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"

Connected through hyperedge HE-6 (induced by source paper "learning from multiple cities a meta learning approach for spatial temporal prediction"):
  with members:
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "crowd flow prediction by deep spatio temporal transfer learning"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "spatial temporal self attention network for flow prediction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "short term traffic flow forecasting with spatial temporal correlation in a hybrid deep learning framework"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-8 (induced by source paper "multi view time series classification via global local correlative channel aware fusion mechanism"):
  with members:
    - "multivariate lstm fcns for time series classification"
    - "ev action electromyography vision multi modal action dataset"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "memory fusion network for multi view sequential learning"
    - "multi scale convolutional neural networks for time series classification"
    - "temporal segment networks towards good practices for deep action recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on recursive edit distance kernels with application to time series classification"

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)