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: "bayesian games and the smoothness framework"
  Abstract: We consider a general class of Bayesian Games where each players utility depends on his type (possibly multidimensional) and on the strategy profile and where players' types are distributed independently. We show that if their full information version for any fixed instance of the type profile is a smooth game then the Price of Anarchy bound implied by the smoothness property, carries over to the Bayes-Nash Price of Anarchy. We show how some proofs from the literature (item bidding auctions, greedy auctions) can be cast as smoothness proofs or be simplified using smoothness. For first price item bidding with fractionally subadditive bidders we actually manage to improve by much the existing result \cite{Hassidim2011a} from 4 to $\frac{e}{e-1}\approx 1.58$. This also shows a very interesting separation between first and second price item bidding since second price item bidding has PoA at least 2 even under complete information. For a larger class of Bayesian Games where the strategy space of a player also changes with his type we are able to show that a slightly stronger definition of smoothness also implies a Bayes-Nash PoA bound. We show how weighted congestion games actually satisfy this stronger definition of smoothness. This allows us to show that the inefficiency bounds of weighted congestion games known in the literature carry over to incomplete versions where the weights of the players are private information. We also show how an incomplete version of a natural class of monotone valid utility games, called effort market games are universally $(1,1)$-smooth. Hence, we show that incomplete versions of effort market games where the abilities of the players and their budgets are private information has Bayes-Nash PoA at most 2.

Connected through hyperedge HE-1 (induced by source paper "fair allocation of indivisible goods improvement and generalization"):
  with members:
    - "fair allocation of indivisible goods to asymmetric agents"
    - "approximation algorithms for computing maximin share allocations"
    - "approximate maximin shares for groups of agents"
    - "combinatorial algorithm for restricted max min fair allocation"
    - "combinatorial auctions via posted prices"
    - "constrained non monotone submodular maximization offline and secretary algorithms"
    - "on allocating goods to maximize fairness"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-2 (induced by source paper "the price of anarchy in auctions"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "simple mechanisms for agents with complements"
    - "the price of anarchy in large games"
    - "on the economic efficiency of the combinatorial clock auction"
    - "on the efficiency of all pay mechanisms"
    - "learning and efficiency in games with dynamic population"
    - "no regret learning in bayesian games"

Connected through hyperedge HE-3 (induced by source paper "descending price coordinates approximately efficient search"):
  with members:
    - "price of anarchy for auction revenue"
    - "price of anarchy for greedy auctions"

Connected through hyperedge HE-4 (induced by source paper "learning and efficiency in games with dynamic population"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "achieving all with no parameters adaptive normalhedge"
    - "inducing approximately optimal flow using truthful mediators"
    - "private pareto optimal exchange"
    - "mirror descent meets fixed share and feels no regret"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-5 (induced by source paper "the price of anarchy in large games"):
  with members:
    - "a unifying hierarchy of valuations with complements and substitutes"
    - "an anti folk theorem for large repeated games with imperfect monitoring"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "mechanism design in large games incentives and privacy"
    - "composable and efficient mechanisms"
    - "simultaneous auctions are almost efficient"
    - "non price equilibria in markets of discrete goods"
    - "price of anarchy for greedy auctions"

Connected through hyperedge HE-6 (induced by source paper "greedy algorithms make efficient mechanisms"):
  with members:
    - "on the efficiency of the walrasian mechanism"
    - "bounding the inefficiency of outcomes in generalized second price auctions"
    - "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-7 (induced by source paper "price of stability in games of incomplete information"):
  with members:
    - "on discrete preferences and coordination"
    - "simultaneous auctions are almost efficient"
    - "non price equilibria in markets of discrete goods"

Connected through hyperedge HE-8 (induced by source paper "when does the price of anarchy tend to 1 in large walrasian auctions and fisher markets"):
  with members:
    - "do prices coordinate markets"
    - "the price of anarchy in large games"
    - "an anti folk theorem for large repeated games with imperfect monitoring"

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)