human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep learning for spatio temporal data mining a survey"):
  Abstract: With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal datasets increase rapidly, traditional data mining methods, especially statistics based methods for dealing with such data are becoming overwhelmed. Recently, with the advances of deep learning techniques, deep leaning models such as convolutional neural network (CNN) and recurrent neural network (RNN) have enjoyed considerable success in various machine learning tasks due to their powerful hierarchical feature learning ability in both spatial and temporal domains, and have been widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, representation learning, anomaly detection and classification. In this paper, we provide a comprehensive survey on recent progress in applying deep learning techniques for STDM. We first categorize the types of spatio-temporal data and briefly introduce the popular deep learning models that are used in STDM. Then a framework is introduced to show a general pipeline of the utilization of deep learning models for STDM. Next we classify existing literatures based on the types of ST data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions.

Includes members:
  Member "t gcn a temporal graph convolutional network for traffic prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adargcn adaptive aggregation gcn for few shot learning")
  Member "learning from multiple cities a meta learning approach for spatial temporal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "inferring transportation modes from gps trajectories using a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "how to eliminate detour behaviors in e hailing real time detecting and time dependent pricing")
  Member "where to go next a spatio temporal lstm model for next poi recommendation":
    also connected through other hyperedges:
      (none)
  Member "forecasting transportation network speed using deep capsule networks with nested lstm models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph markov network for traffic forecasting with missing data")
  Member "a hybrid method for traffic flow forecasting using multimodal deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph partitioning based diffusion convolution recurrent neural network for large scale traffic forecasting")
  Member "deep sequence learning with auxiliary information for traffic prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "building effective large scale traffic state prediction system traffic4cast challenge solution")

Question: Please tell me which class the source paper 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)