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: "bounding the inefficiency of outcomes in generalized second price auctions"
  Abstract: The Generalized Second Price (GSP) auction is the primary auction used for monetizing the use of the Internet. It is well-known that truthtelling is not a dominant strategy in this auction and that inefficient equilibria can arise. In this paper we study the space of equilibria in GSP, and quantify the efficiency loss that can arise in equilibria under a wide range of sources of uncertainty, as well as in the full information setting. The traditional Bayesian game models uncertainty in the valuations (types) of the participants. The Generalized Second Price (GSP) auction gives rise to a further form of uncertainty: the selection of quality factors resulting in uncertainty about the behavior of the underlying ad allocation algorithm. The bounds we obtain apply to both forms of uncertainty, and are robust in the sense that they apply under various perturbations of the solution concept, extending to models with information asymmetries and bounded rationality in the form of learning strategies. #R##N#We present a constant bound (2.927) on the factor of the efficiency loss (\emph{price of anarchy}) of the corresponding game for the Bayesian model of partial information about other participants and about ad quality factors. For the full information setting, we prove a surprisingly low upper bound of 1.282 on the price of anarchy over pure Nash equilibria, nearly matching a lower bound of 1.259 for the case of three advertisers. Further, we do not require that the system reaches equilibrium, and give similarly low bounds also on the quality degradation for any no-regret learning outcome. Our conclusion is that the number of advertisers in the auction has almost no impact on the price of anarchy, and that the efficiency of GSP is very robust with respect to the belief and rationality assumptions imposed on the participants.

Connected through hyperedge HE-1 (induced by source paper "on the tractability of public persuasion with no externalities"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "no regret learning in bayesian games"
    - "algorithmic bayesian persuasion"
    - "deterministic algorithms for submodular maximization problems"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "honest signaling in zero sum games is hard and lying is even harder"
    - "mixture selection mechanism design and signaling"

Connected through hyperedge HE-2 (induced by source paper "when smoothness is not enough toward exact quantification and optimization of the price of anarchy"):
  with members:
    - "optimal price of anarchy in cost sharing games"
    - "distributed resource allocation through utility design part i optimizing the performance certificates via the price of anarchy"
    - "utility design for distributed resource allocation part i characterizing and optimizing the exact price of anarchy"
    - "the impact of local information on the performance of multiagent systems"
    - "multiagent maximum coverage problems the trade off between anarchy and stability"
    - "learning in games robustness of fast convergence"
    - "the price of anarchy in auctions"
    - "composable and efficient mechanisms"

Connected through hyperedge HE-3 (induced by source paper "the pareto frontier of inefficiency in mechanism design"):
  with members:
    - "multiagent maximum coverage problems the trade off between anarchy and stability"
    - "correlated and coarse equilibria of single item auctions"
    - "the anarchy of scheduling without money"
    - "the price of anarchy in auctions"
    - "setting lower bounds on truthfulness"
    - "the vcg mechanism for bayesian scheduling"
    - "social welfare in one sided matching mechanisms"
    - "bayesian truthful mechanisms for job scheduling from bi criterion approximation algorithms"

Connected through hyperedge HE-4 (induced by source paper "a note on the efficiency of position mechanisms with budget constraints"):
  with members:
    - "the efficiency of resource allocation mechanisms for budget constrained users"
    - "the price of anarchy in auctions"
    - "on the efficiency of the proportional allocation mechanism for divisible resources"
    - "liquid price of anarchy"
    - "improved efficiency guarantees in auctions with budgets"
    - "welfare guarantees for proportional allocations"
    - "efficiency guarantees in auctions with budgets"
    - "on the stability of generalized second price auctions with budgets"

Connected through hyperedge HE-5 (induced by source paper "learning to bid without knowing your value"):
  with members:
    - "bandits with movement costs and adaptive pricing"
    - "revenue optimization with approximate bid predictions"
    - "online learning with feedback graphs without the graphs"
    - "the sample complexity of revenue maximization"
    - "learning and efficiency in games with dynamic population"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"
    - "online learning and profit maximization from revealed preferences"

Connected through hyperedge HE-6 (induced by source paper "smoothness for simultaneous composition of mechanisms with admission"):
  with members:
    - "learning and efficiency in games with dynamic population"
    - "no regret learning in bayesian games"
    - "learning in auctions regret is hard envy is easy"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "jamming resistant learning in wireless networks"
    - "tight bounds for the price of anarchy of simultaneous first price auctions"
    - "composable and efficient mechanisms"
    - "simultaneous auctions are almost efficient"

Connected through hyperedge HE-7 (induced by source paper "greedy algorithms make efficient mechanisms"):
  with members:
    - "on the efficiency of the walrasian mechanism"
    - "bayesian games and the smoothness framework"
    - "polyhedral clinching auctions and the adwords polytope"
    - "composable and efficient mechanisms"
    - "non price equilibria in markets of discrete goods"
    - "price of anarchy for greedy auctions"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-8 (induced by source paper "no regret learning in bayesian games"):
  with members:
    - "composable and efficient mechanisms"
    - "price of anarchy for greedy 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)