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 "feedback loops of attention in peer production"):
  Abstract: A significant percentage of online content is now published and consumed via the mechanism of crowdsourcing. While any user can contribute to these forums, a disproportionately large percentage of the content is submitted by very active and devoted users, whose continuing participation is key to the sites' success. As we show, people's propensity to keep participating increases the more they contribute, suggesting motivating factors which increase over time. This paper demonstrates that submitters who stop receiving attention tend to stop contributing, while prolific contributors attract an ever increasing number of followers and their attention in a feedback loop. We demonstrate that this mechanism leads to the observed power law in the number of contributions per user and support our assertions by an analysis of hundreds of millions of contributions to top content sharing websites Digg.com and Youtube.com.

Includes members:
  Member "crowdsourcing attention and productivity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "benford s law a sleeping beauty sleeping in the dirty pages of logarithmic tables")
  Member "predicting the popularity of online content":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online convex optimization for caching networks")
  Member "novelty and collective attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling the popularity of twitter hashtags with master equations")
  Member "social networks and social information filtering on digg":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "systems applications of social networks")

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)