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 "the size conundrum why online knowledge markets can fail at scale"):
  Abstract: In this paper, we interpret the community question answering websites on the StackExchange platform as knowledge markets, and analyze how and why these markets can fail at scale. A knowledge market framing allows site operators to reason about market failures, and to design policies to prevent them. Our goal is to provide insights on large-scale knowledge market failures through an interpretable model. We explore a set of interpretable economic production models on a large empirical dataset to analyze the dynamics of content generation in knowledge markets. Amongst these, the Cobb-Douglas model best explains empirical data and provides an intuitive explanation for content generation through concepts of elasticity and diminishing returns. Content generation depends on user participation and also on how specific types of content (e.g. answers) depends on other types (e.g. questions). We show that these factors of content generation have constant elasticity—a percentage increase in any of the inputs leads to a constant percentage increase in the output. Furthermore, markets exhibit diminishing returns—the marginal output decreases as the input is incrementally increased. Knowledge markets also vary on their returns to scale—the increase in output resulting from a proportionate increase in all inputs. Importantly, many knowledge markets exhibit diseconomies of scale—measures of market health (e.g., the percentage of questions with an accepted answer) decrease as a function of number of participants. The implications of our work are two-fold: site operators ought to design incentives as a function of system size (number of participants); the market lens should shed insight into complex dependencies amongst different content types and participant actions in general social networks.

Includes members:
  Member "community aliveness discovering interaction decay patterns in online social communities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "postmortem analysis of decayed online social communities cascade pattern analysis and prediction")
  Member "uncovering the dynamics of crowdlearning and the value of knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distilling information reliability and source trustworthiness from digital traces")
  Member "dynamics of content quality in collaborative knowledge production":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying the impact of cognitive biases in question answering systems")
  Member "wisdom of the crowd or wisdom of a few an analysis of users content generation":
    also connected through other hyperedges:
      (none)
  Member "activity dynamics in collaboration networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "activity archetypes in question and answer q a websites a study of 50 stack exchange instances")
  Member "social resilience in online communities the autopsy of friendster":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "span core decomposition for temporal networks algorithms and applications")

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)