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: "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
  Abstract: Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice, the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackles both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.

Connected through hyperedge HE-1 (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"
    - "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"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-2 (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"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "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-3 (induced by source paper "joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values"):
  with members:
    - "megan a generative adversarial network for multi view network embedding"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "joint modeling of dense and incomplete trajectories for citywide traffic volume inference"
    - "brits bidirectional recurrent imputation for time series"
    - "a memory network based solution for multivariate time series forecasting"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "end to end learning for short text expansion"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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-5 (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"
    - "deep multi view spatial temporal network for taxi demand 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-6 (induced by source paper "event ticket price prediction with deep neural network on spatial temporal sparse data"):
  with members:
    - "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"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "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-7 (induced by source paper "colight learning network level cooperation for traffic signal control"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "a survey on traffic signal control methods"
    - "a deep reinforcement learning network for traffic light cycle control"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "mean field multi agent reinforcement learning"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "attention is all you need"
    - "deep deterministic policy gradient for urban traffic light control"

Connected through hyperedge HE-8 (induced by source paper "joint modeling of dense and incomplete trajectories for citywide traffic volume inference"):
  with members:
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "an attention based collaboration framework for multi view network representation learning"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"
    - "inferring uncertain trajectories from partial observations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)