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: "predicting regional economic indices using big data of individual bank card transactions"
  Abstract: For centuries quality of life was a subject of studies across different disciplines. However, only with the emergence of a digital era, it became possible to investigate this topic on a larger scale. Over time it became clear that quality of life not only depends on one, but on three relatively different parameters: social, economic and well-being measures. In this study we focus only on the first two, since the last one is often very subjective and consequently hard to measure. Using a complete set of bank card transactions recorded by Banco Bilbao Vizcaya Argentaria (BBVA) during 2011 in Spain, we first create a feature space by defining various meaningful characteristics of a particular area performance through activity of its businesses, residents and visitors. We then evaluate those quantities by considering available official statistics for Spanish provinces (e.g., housing prices, unemployment rate, life expectancy) and investigate whether they can be predicted based on our feature space. For the purpose of prediction, our study proposes a supervised machine learning approach. Our finding is that there is a clear correlation between individual spending behavior and official socioeconomic indexes denoting quality of life. Moreover, we believe that this modus operandi is useful to understand, predict and analyze the impact of human activity on the wellness of our society on scales for which there is no consistent official statistics available (e.g., cities and towns, districts or smaller neighborhoods).

Connected through hyperedge HE-1 (induced by source paper "impact of urban technology deployments on local commercial activity"):
  with members:
    - "urban magnetism through the lens of geo tagged photography"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "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-2 (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"
    - "urban magnetism through the lens of geo tagged photography"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "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-3 (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"
    - "urban magnetism through the lens of geo tagged photography"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "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-4 (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"
    - "urban magnetism through the lens of geo tagged photography"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"

Connected through hyperedge HE-5 (induced by source paper "choosing the right home location definition method for the given dataset"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "urban magnetism through the lens of geo tagged photography"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "geo located twitter as proxy for global mobility patterns"
    - "where in the world are you geolocation and language identification in twitter"
    - "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"
    - "multiple location profiling for users and relationships from social network and content"

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)