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: "targeting and signaling in ad auctions"
  Abstract: Modern ad auctions allow advertisers to target more specific segments of the user population. Unfortunately, this is not always in the best interest of the ad platform. In this paper, we examine the following basic question in the context of second-price ad auctions: how should an ad platform optimally reveal information about the ad opportunity to the advertisers in order to maximize revenue? We consider a model in which bidders' valuations depend on a random state of the ad opportunity. Different from previous work, we focus on a more practical, and challenging, situation where the space of possible realizations of ad opportunities is extremely large. We thus focus on developing algorithms whose running time is independent of the number of ad opportunity realizations. #R##N#We examine the auctioneer's algorithmic question of designing the optimal signaling scheme. When the auctioneer is restricted to send a public signal to all bidders, we focus on a well-motivated Bayesian valuation setting in which the auctioneer and bidders both have private information, and present two main results: 1. we exhibit a characterization result regarding approximately optimal schemes and prove that any constant-approximate public signaling scheme must use exponentially many signals; 2. we present a "simple" public signaling scheme that serves as a constant approximation under mild assumptions. We then initiate an exploration on the power of being able to send different signals privately to different bidders. Here we examine a basic setting where the auctioneer knows bidders' valuations, and exhibit a polynomial-time private scheme that extracts almost full surplus even in the worst Bayes Nash equilibrium. This illustrates the surprising power of private signaling schemes in extracting revenue.

Connected through hyperedge HE-1 (induced by source paper "public bayesian persuasion being almost optimal and almost persuasive"):
  with members:
    - "persuading voters it s easy to whisper it s hard to speak loud"
    - "on the tractability of public persuasion with no externalities"
    - "algorithmic persuasion with no externalities"
    - "computational aspects of private bayesian persuasion"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "inapproximability results for approximate nash equilibria"
    - "can almost everybody be almost happy pcp for ppad and the inapproximability of nash"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-2 (induced by source paper "bayesian persuasion with sequential games"):
  with members:
    - "computational aspects of private bayesian persuasion"
    - "no regret learning in bayesian games"
    - "algorithmic bayesian persuasion"
    - "constrained signaling in auction design"
    - "sequential auctions and externalities"

Connected through hyperedge HE-3 (induced by source paper "information design in spatial resource competition"):
  with members:
    - "information signal design for incentivizing team formation"
    - "algorithmic persuasion with no externalities"
    - "computational aspects of private bayesian persuasion"

Connected through hyperedge HE-4 (induced by source paper "selling information through consulting"):
  with members:
    - "algorithmic bayesian persuasion"
    - "send mixed signals earn more work less"
    - "optimal mechanisms for selling information"

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)