human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "aiads automated and intelligent advertising system for sponsored search"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "roi constrained auctions"
    - "optimized cost per click in taobao display advertising"
    - "sponsored search auctions with rich ads"
    - "scalable semantic matching of queries to ads in sponsored search advertising"
    - "fast core pricing for rich advertising auctions"
    - "smart pacing for effective online ad campaign optimization"

Connected through hyperedge HE-2 (induced by source paper "cost per action constrained auctions"):
  with members:
    - "pacing equilibrium in first price auction markets"
    - "multiplicative pacing equilibria in auction markets"
    - "gsp the cinderella of mechanism design"
    - "real time bidding by reinforcement learning in display advertising"

Connected through hyperedge HE-3 (induced by source paper "roi constrained auctions"):
  with members:
    - "real time bidding by reinforcement learning in display advertising"

Connected through hyperedge HE-4 (induced by source paper "sponsored search auctions with rich ads"):
  with members:
    - "gsp the cinderella of mechanism design"
    - "truthful outcomes from non truthful position auctions"
    - "gsp with general independent click through rates"
    - "optimising trade offs among stakeholders in ad auctions"
    - "a dynamic axiomatic approach to first price auctions"

Question: Please tell me which class the center node 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)