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: "analysis of tourism dynamics and special events through mobile phone metadata"
  Abstract: Tourism has been an increasingly important factor in global economy, society and environment, accounting for a significant share of GDP and labor force. Policy and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at low spatial and temporal granularity. Anonymous call detail records (CDRs) are a novel source of data, showing enormous potential in areas of high societal value: such as epidemics, poverty, and urban development. This study demonstrates the added value of using CDRs for the formulation, analysis and evaluation of tourism strategies, at the national and local levels. In the context of the European country of Andorra, we use CDRs to evaluate marketing strategies in tourism, understand tourists' experiences, and evaluate revenues and externalities generated by touristic events. We do this by extracting novel indicators in high spatial and temporal resolutions, such as tourist flows per country of origin, flows of new tourists, tourist revisits, tourist externalities on transportation congestion, spatial distribution, economic impact, and profiling of tourist interests. We exemplify the use of these indicators for the planning and evaluation of high impact touristic events, such as cultural festivals and sports competitions.

Connected through hyperedge HE-1 (induced by source paper "profiling presence patterns and segmenting user locations from cell phone data"):
  with members:
    - "choosing the right home location definition method for the given dataset"
    - "exploring universal patterns in human home work commuting from mobile phone data"

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)