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 "global multi layer network of human mobility"):
  Abstract: Recent availability of geo-localized data capturing individual human activity together with the statistical data on international migration opened up unprecedented opportunities for a study on global mobility. In this paper we consider it from the perspective of a multi-layer complex network, built using a combination of three datasets: Twitter, Flickr and official migration data. Those datasets provide different but equally important insights on the global mobility: while the first two highlight short-term visits of people from one country to another, the last one - migration - shows the long-term mobility perspective, when people relocate for good. And the main purpose of the paper is to emphasize importance of this multi-layer approach capturing both aspects of human mobility at the same time. So we start from a comparative study of the network layers, comparing short- and long- term mobility through the statistical properties of the corresponding networks, such as the parameters of their degree centrality distributions or parameters of the corresponding gravity model being fit to the network. We also focus on the differences in country ranking by their short- and long-term attractiveness, discussing the most noticeable outliers. Finally, we apply this multi-layered human mobility network to infer the structure of the global society through a community detection approach and demonstrate that consideration of mobility from a multi-layer perspective can reveal important global spatial patterns in a way more consistent with other available relevant sources of international connections, in comparison to the spatial structure inferred from each network layer taken separately.

Includes members:
  Member "scaling of foreign attractiveness for countries and states":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extracting human emotions at different places based on facial expressions and spatial clustering analysis")
  Member "yfcc100m the new data in multimedia research":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "in defense of grid features for visual question answering")
  Member "choosing the right home location definition method for the given dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "assessing the quality of home detection from mobile phone data for official statistics")
  Member "predicting regional economic indices using big data of individual bank card transactions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "impact of urban technology deployments on local commercial activity")
  Member "urban magnetism through the lens of geo tagged photography":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fine grained land use classification at the city scale using ground level images")
  Member "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scaling laws in geo located twitter data")
  Member "geo located twitter as proxy for global mobility patterns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection")
  Member "general optimization technique for high quality community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection")

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)