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 "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"):
  Abstract: Scientific studies investigating laws and regularities of human behavior are nowadays increasingly relying on the wealth of widely available digital information produced by human activity. In this paper we use big data created by three different aspects of this activity (i.e., Bank card transactions, geotagged photographs and tweets) in Spain for quantifying city attractiveness for foreign visitors. An important finding of this paper is a strong super linear scaling law of city attractiveness with its population size. The observed scaling exponent stays nearly the same for different ways of defining cities and for different data sources, emphasizing the robustness of our finding. We also consider temporal variation of the scaling exponent in order to reveal seasonal patterns in the attractiveness. Finally, we propose a possible explanatory mechanism for the observed super linear effect based on a simple discrete choice model.

Includes members:
  Member "urban magnetism through the lens of geo tagged photography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fine grained land use classification at the city scale using ground level images")
  Member "geo located twitter as proxy for global mobility patterns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection")
  Member "exploring universal patterns in human home work commuting from mobile phone data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection")
  Member "delineating geographical regions with networks of human interactions in an extensive set of countries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "assessing the quality of home detection from mobile phone data for official statistics")

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.SI(Social and Information Networks)