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: "predicting citywide crowd flows using deep spatio temporal residual networks"
  Abstract: Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e.g., weather and events). We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i.e. inflow and outflow) in each and every region of a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We have developed a real-time system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd flow monitoring and forecasting in Guiyang City of China. In addition, we present an extensive experimental evaluation using two types of crowd flows in Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known baselines.

Connected through hyperedge HE-1 (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-2 (induced by source paper "st unet a spatio temporal u network for graph structured time series modeling"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "large scale learnable graph convolutional networks"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "dilated recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-3 (induced by source paper "predicting citywide crowd flows in irregular regions using multi view graph convolutional networks"):
  with members:
    - "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"
    - "unsupervised learning of video representations using lstms"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"

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"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "fairst equitable spatial and temporal demand prediction for new mobility systems"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "a convex framework for fair regression"
    - "equality of opportunity in supervised learning"
    - "tensorflow a system for large scale machine learning"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "going deeper with convolutions"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "urban flows prediction from spatial temporal data using machine learning a survey"):
  with members:
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "bike flow prediction with multi graph convolutional networks"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "spatio temporal data mining a survey of problems and methods"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "star a concise deep learning framework for citywide human mobility prediction"):
  with members:
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "identity mappings in deep residual networks"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "spatiotemporal attention networks for wind power forecasting"):
  with members:
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)