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: "cross city transfer learning for deep spatio temporal prediction"
  Abstract: Spatio-temporal prediction is a key type of tasks in urban computing, e.g., traffic flow and air quality. Adequate data is usually a prerequisite, especially when deep learning is adopted. However, the development levels of different cities are unbalanced, and still many cities suffer from data scarcity. To address the problem, we propose a novel cross-city transfer learning method for deep spatio-temporal prediction tasks, called RegionTrans. RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city. More specifically, we first learn an inter-city region matching function to match each target city region to a similar source city region. A neural network is designed to effectively extract region-level representation for spatio-temporal prediction. Finally, an optimization algorithm is proposed to transfer learned features from the source city to the target city with the region matching function. Using citywide crowd flow prediction as a demonstration experiment, we verify the effectiveness of RegionTrans. Results show that RegionTrans can outperform the state-of-the-art fine-tuning deep spatio-temporal prediction models by reducing up to 10.7% prediction error.

Connected through hyperedge HE-1 (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"
    - "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"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"

Connected through hyperedge HE-2 (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"
    - "deep multi view spatial temporal network for taxi demand 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-3 (induced by source paper "urbanfm inferring fine grained urban flows"):
  with members:
    - "crowd flow prediction by deep spatio temporal transfer learning"
    - "enhanced deep residual networks for single image super resolution"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "predicting citywide crowd flows using deep spatio temporal residual networks"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "attentive crowd flow machines"):
  with members:
    - "crowd counting using deep recurrent spatial aware network"
    - "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"
    - "scalable deep traffic flow neural networks for urban traffic congestion prediction"

Connected through hyperedge HE-6 (induced by source paper "smart city development with urban transfer learning"):
  with members:
    - "a survey on multi task learning"
    - "ridesourcing car detection by transfer learning"

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)