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: "a dynamic axiomatic approach to first price auctions"
  Abstract: The first-price auction is popular in practice for its simplicity and transparency. Moreover, its potential virtues grow in complex settings where incentive compatible auctions may generate little or no revenue. Unfortunately, the first-price auction is poorly understood in theory because equilibrium is not {\em a priori} a credible predictor of bidder behavior. #R##N#We take a dynamic approach to studying first-price auctions: rather than basing performance guarantees solely on static equilibria, we study the repeated setting and show that robust performance guarantees may be derived from simple axioms of bidder behavior. For example, as long as a loser raises her bid quickly, a standard first-price auction will generate at least as much revenue as a second-price auction. We generalize this dynamic technique to complex pay-your-bid auction settings and show that progressively stronger assumptions about bidder behavior imply progressively stronger guarantees about the auction's performance. #R##N#Along the way, we find that the auctioneer's choice of bidding language is critical when generalizing beyond the single-item setting, and we propose a specific construction called the {\em utility-target auction} that performs well. The utility-target auction includes a bidder's final utility as an additional parameter, identifying the single dimension along which she wishes to compete. This auction is closely related to profit-target bidding in first-price and ascending proxy package auctions and gives strong revenue guarantees for a variety of complex auction environments. Of particular interest, the guaranteed existence of a pure-strategy equilibrium in the utility-target auction shows how Overture might have eliminated the cyclic behavior in their generalized first-price sponsored search auction if bidders could have placed more sophisticated bids.

Connected through hyperedge HE-1 (induced by source paper "sponsored search auctions with rich ads"):
  with members:
    - "gsp the cinderella of mechanism design"
    - "mechanism design for value maximizers"
    - "truthful outcomes from non truthful position auctions"
    - "gsp with general independent click through rates"
    - "optimising trade offs among stakeholders in ad auctions"

Connected through hyperedge HE-2 (induced by source paper "descending price algorithm for determining market clearing prices in matching markets"):
  with members:
    - "note on vcg vs price raising for matching markets"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "the complexity of optimal mechanism design"

Connected through hyperedge HE-3 (induced by source paper "efficient advert assignment"):
  with members:
    - "econometrics for learning agents"
    - "optimising trade offs among stakeholders in ad auctions"
    - "bayesian sequential auctions"
    - "simplicity expressiveness tradeoffs in mechanism design"
    - "online advertisement optimization and stochastic networks"
    - "truthful mechanisms with implicit payment computation"
    - "bidding to the top vcg and equilibria of position based 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)