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: "tourists digital footprint in cities comparing big data sources"
  Abstract: There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data (Big Data) when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected). Tourist density in the three data sources is compared via maps, correlation analysis (OLS) and spatial self-correlation analysis (Global Moran's I statistic and LISA). Finally the data are integrated using cluster analysis and combining the spatial clusters identified in the LISA analysis in the different data sources. The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces (with several activities) and spaces specialising in one or various activities (for example, sightseeing and consumption). The case study analysed (Madrid) reveals a significant presence of tourists in the city centre, and increasing specialisation from the centre outwards towards the periphery. The main conclusion of the paper is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.

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.CY(Computers and Society)