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: "spatio temporal data mining a survey of problems and methods"
  Abstract: Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data mining community. In this article we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data mining problems in each of these categories.

Connected through hyperedge HE-1 (induced by source paper "semi supervised classification using attention based regularization on coarse resolution data"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "attention based deep multiple instance learning"
    - "neural machine translation by jointly learning to align and translate"
    - "a survey on multi view learning"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (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-3 (induced by source paper "assessing refugees integration via spatio temporal similarities of mobility and calling behaviors"):
  with members:
    - "detecting permanent and intermittent purchase hotspots via computational stigmergy"
    - "data for refugees the d4r challenge on mobility of syrian refugees in turkey"
    - "a stigmergy based analysis of city hotspots to discover trends and anomalies in urban transportation usage"
    - "studying migrant assimilation through facebook interests"
    - "stigmergy based modeling to discover urban activity patterns from positioning data"
    - "crisis analytics big data driven crisis response"
    - "mobile communication signatures of unemployment"
    - "an analytical framework to nowcast well being using mobile phone data"

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"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "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"
    - "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 "a convolution recurrent autoencoder for spatio temporal missing data imputation"):
  with members:
    - "a spatial temporal decomposition based deep neural network for time series forecasting"
    - "brits bidirectional recurrent imputation for time series"
    - "mida multiple imputation using denoising autoencoders"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-6 (induced by source paper "a graph structured dataset for wikipedia research"):
  with members:
    - "mining missing hyperlinks from human navigation traces a case study of wikipedia"
    - "using triangles to improve community detection in directed networks"

Connected through hyperedge HE-7 (induced by source paper "anomaly detection in the dynamics of web and social networks"):
  with members:
    - "principal patterns on graphs discovering coherent structures in datasets"

Connected through hyperedge HE-8 (induced by source paper "transportation modes classification using feature engineering"):
  with members:
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "predicting transportation modes of gps trajectories using feature engineering and noise removal"
    - "predicting crime using spatial features"
    - "feature selection a data perspective"
    - "trajectorynet an embedded gps trajectory representation for point based classification using recurrent neural 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.LG(Machine Learning)