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 "extracting patterns of urban activity from geotagged social data"):
  Abstract: Data generated on location-based social networks provide rich information on the whereabouts of urban dwellers. Specifically, such data reveal who spends time where, when, and on what type of activity (e.g., shopping at a mall, or dining at a restaurant). That information can, in turn, be used to describe city regions in terms of activity that takes place therein. For example, the data might reveal that citizens visit one region mainly for shopping in the morning, while another for dining in the evening. Furthermore, once such a description is available, one can ask more elaborate questions: What are the features that distinguish one region from another -- is it simply the type of venues they host or is it the visitors they attract? What regions are similar across cities? #R##N#In this paper, we attempt to answer these questions using publicly shared Foursquare data. In contrast with previous work, our method makes use of a probabilistic model with minimal assumptions about the data and thus relieves us from having to make arbitrary decisions in our analysis (e.g., regarding the granularity of discovered regions or the importance of different features). We perform an empirical comparison with previous work and discuss insights obtained through our findings.

Includes members:
  Member "geo sage a geographical sparse additive generative model for spatial item recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relation embedding for personalised poi recommendation")
  Member "on the accuracy of hyper local geotagging of social media content":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of location prediction on twitter")
  Member "hoodsquare modeling and recommending neighborhoods in location based social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sensing and modeling human behavior using social media and mobile data")

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.CY(Computers and Society)