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 "mechanism design for value maximizers"):
  Abstract: Bidders often want to get as much as they can without violating constraints on what they spend. For example, advertisers seek to maximize the impressions, clicks, sales, or market share generated by their advertising, subject to budget or return-on-investment (ROI) constraints. The quasilinear utility model dramatically fails to capture these preferences, and so we initiate the study of mechanism design in this different context. In single-parameter settings, we show that any monotone allocation can be implemented truthfully. Interestingly, even in unrestricted domains any choice function that optimizes monotone functions of bidders' values can be implemented. For general valuations we show that maximizing the value of the highest-value bidder is the natural analog of welfare maximization. #R##N#We apply our results to online advertising as a case study. Firstly, the natural analog of welfare maximization directly generalizes the generalized second price (GSP) auction commonly used to sell search ads. Finally, we show that value-maximizing preferences are robust in a practical sense: even though real advertisers' preferences are complex and varied, as long as outcomes are "sufficiently different," any advertiser with a moderate ROI constraint and "super-quasilinear" preferences will be behaviorally equivalent to a value maximizer. We empirically establish that for at least 80% of a sample of auctions from the Yahoo Gemini Native ads platform, bidders requiring at least a 100% ROI should behave like value maximizers.

Includes members:
  Member "web and internet economics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how to re divide a cake fairly")
  Member "optimising trade offs among stakeholders in ad auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a price per attention auction scheme using mouse cursor information")
  Member "general auction mechanism for search advertising":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders")

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.GT(Computer Science and Game Theory)