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: "crowd flow prediction by deep spatio temporal transfer learning"
  Abstract: Crowd flow prediction is a fundamental urban computing problem. Recently, deep learning has been successfully applied to solve this problem, but it relies on rich historical data. In reality, many cities may suffer from data scarcity issue when their targeted service or infrastructure is new. To overcome this issue, this paper proposes a novel deep spatio-temporal transfer learning framework, called RegionTrans, which can predict future crowd flow in a data-scarce (target) city by transferring knowledge from a data-rich (source) city. Leveraging social network check-ins, RegionTrans first links a region in the target city to certain regions in the source city, expecting that these inter-city region pairs will share similar crowd flow dynamics. Then, we propose a deep spatio-temporal neural network structure, in which a hidden layer is dedicated to keeping the region representation. A source city model is then trained on its rich historical data with this network structure. Finally, we propose a region-based cross-city transfer learning algorithm to learn the target city model from the source city model by minimizing the hidden representation discrepancy between the inter-city region pairs previously linked by check-ins. With experiments on real crowd flow, RegionTrans can outperform state-of-the-arts 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"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "attentive crowd flow machines"
    - "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"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "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:
    - "cross city transfer learning for deep spatio temporal prediction"
    - "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"

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)