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: "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.

Connected through hyperedge HE-1 (induced by source paper "scaling laws in geo located twitter data"):
  with members:
    - "social sensing of floods in the uk"
    - "a survey of location inference techniques on twitter"
    - "confounds and consequences in geotagged twitter data"
    - "performance of social network sensors during hurricane sandy"
    - "geo located twitter as proxy for global mobility patterns"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-2 (induced by source paper "impact of urban technology deployments on local commercial activity"):
  with members:
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"
    - "geo located twitter as proxy for global mobility patterns"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"

Connected through hyperedge HE-3 (induced by source paper "tourists digital footprint in cities comparing big data sources"):
  with members:
    - "the use of public spaces in a medium sized city from twitter data to mobility patterns"
    - "extracting patterns of urban activity from geotagged social data"
    - "geo located twitter as proxy for global mobility patterns"

Connected through hyperedge HE-4 (induced by source paper "twitter activity timeline as a signature of urban neighborhood"):
  with members:
    - "urban magnetism through the lens of geo tagged photography"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"

Connected through hyperedge HE-5 (induced by source paper "the use of public spaces in a medium sized city from twitter data to mobility patterns"):
  with members:
    - "geo located twitter as proxy for global mobility patterns"
    - "multi scale population and mobility estimation with geo tagged tweets"

Connected through hyperedge HE-6 (induced by source paper "global multi layer network of human mobility"):
  with members:
    - "scaling of foreign attractiveness for countries and states"
    - "yfcc100m the new data in multimedia research"
    - "choosing the right home location definition method for the given dataset"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"
    - "geo located twitter as proxy for global mobility patterns"
    - "general optimization technique for high quality community detection in complex networks"
    - "exploring universal patterns in human home work commuting from mobile phone data"

Connected through hyperedge HE-7 (induced by source paper "scaling of foreign attractiveness for countries and states"):
  with members:
    - "global multi layer network of human mobility"
    - "yfcc100m the new data in multimedia research"
    - "sublinear scaling of country attractiveness observed from flickr dataset"
    - "choosing the right home location definition method for the given dataset"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"
    - "geo located twitter as proxy for global mobility patterns"
    - "exploring universal patterns in human home work commuting from mobile phone data"

Connected through hyperedge HE-8 (induced by source paper "sublinear scaling of country attractiveness observed from flickr dataset"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "choosing the right home location definition method for the given dataset"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"

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.SI(Social and Information Networks)