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 "mapping the potential and pitfalls of data dividends as a means of sharing the profits of artificial intelligence"):
  Abstract: Identifying strategies to more broadly distribute the economic winnings of AI technologies is a growing priority in HCI and other fields. One idea gaining prominence centers on "data dividends", or sharing the profits of AI technologies with the people who generated the data on which these technologies rely. Despite the rapidly growing discussion around data dividends - including backing by prominent politicians - there exists little guidance about how data dividends might be designed and little information about if they will work. In this paper, we begin the process of developing a concrete design space for data dividends. We additionally simulate the effects of a variety of important design decisions using well-known datasets and algorithms. We find that seemingly innocuous decisions can create counterproductive effects, e.g. severely concentrated dividends and demographic disparities. Overall, the outcomes we observe -- both desirable and undesirable -- highlight the need for dividend implementers to make design decisions cautiously.

Includes members:
  Member "measuring the importance of user generated content to search engines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "auditing news curation systems a case study examining algorithmic and editorial logic in apple news")
  Member "towards efficient data valuation based on the shapley value":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a distributional framework for data valuation")
  Member "efficient task specific data valuation for nearest neighbor algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a distributional framework for data valuation")
  Member "a data driven analysis of workers earnings on amazon mechanical turk":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "low cost learning via active data procurement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards data auctions with externalities")
  Member "incentivizing high quality crowdwork":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crowdsourcing the perception of machine teaching")
  Member "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "temporal probability calibration")

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)