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 "optimizing trade offs among stakeholders in real time bidding by incorporating multimedia metrics"):
  Abstract: Displaying banner advertisements (in short, ads) on webpages has usually been discussed as an Internet economics topic where a publisher uses auction models to sell an online user's page view to advertisers and the one with the highest bid can have her ad displayed to the user. This is also called \emph{real-time bidding} (RTB) and the ad displaying process ensures that the publisher's benefit is maximized or there is an equilibrium in ad auctions. However, the benefits of the other two stakeholders -- the advertiser and the user -- have been rarely discussed. In this paper, we propose a two-stage computational framework that selects a banner ad based on the optimized trade-offs among all stakeholders. The first stage is still auction based and the second stage re-ranks ads by considering the benefits of all stakeholders. Our metric variables are: the publisher's revenue, the advertiser's utility, the ad memorability, the ad click-through rate (CTR), the contextual relevance, and the visual saliency. To the best of our knowledge, this is the first work that optimizes trade-offs among all stakeholders in RTB by incorporating multimedia metrics. An algorithm is also proposed to determine the optimal weights of the metric variables. We use both ad auction datasets and multimedia datasets to validate the proposed framework. Our experimental results show that the publisher can significantly improve the other stakeholders' benefits by slightly reducing her revenue in the short-term. In the long run, advertisers and users will be more engaged, the increased demand of advertising and the increased supply of page views can then boost the publisher's revenue.

Includes members:
  Member "risk aware dynamic reserve prices of programmatic guarantee in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous")
  Member "a dynamic pricing model for unifying programmatic guarantee and real time bidding in display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous")
  Member "optimising trade offs among stakeholders in ad auctions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a price per attention auction scheme using mouse cursor information")
  Member "a lattice framework for pricing display advertisement options with the stochastic volatility underlying model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous")
  Member "ranking and tradeoffs in sponsored search auctions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a price per attention auction scheme using mouse cursor information")
  Member "multi keyword multi click advertisement option contracts for sponsored search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pricing average price advertising options when underlying spot market prices are discontinuous")

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)