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: "asymptotically truthful equilibrium selection in large congestion games"
  Abstract: Studying games in the complete information model makes them analytically tractable. However, large $n$ player interactions are more realistically modeled as games of incomplete information, where players may know little to nothing about the types of other players. Unfortunately, games in incomplete information settings lose many of the nice properties of complete information games: the quality of equilibria can become worse, the equilibria lose their ex-post properties, and coordinating on an equilibrium becomes even more difficult. Because of these problems, we would like to study games of incomplete information, but still implement equilibria of the complete information game induced by the (unknown) realized player types. #R##N#This problem was recently studied by Kearns et al. and solved in large games by means of introducing a weak mediator: their mediator took as input reported types of players, and output suggested actions which formed a correlated equilibrium of the underlying game. Players had the option to play independently of the mediator, or ignore its suggestions, but crucially, if they decided to opt-in to the mediator, they did not have the power to lie about their type. In this paper, we rectify this deficiency in the setting of large congestion games. We give, in a sense, the weakest possible mediator: it cannot enforce participation, verify types, or enforce its suggestions. Moreover, our mediator implements a Nash equilibrium of the complete information game. We show that it is an (asymptotic) ex-post equilibrium of the incomplete information game for all players to use the mediator honestly, and that when they do so, they end up playing an approximate Nash equilibrium of the induced complete information game. In particular, truthful use of the mediator is a Bayes-Nash equilibrium in any Bayesian game for any prior.

Connected through hyperedge HE-1 (induced by source paper "differentially private call auctions and market impact"):
  with members:
    - "differentially private double spectrum auction with approximate social welfare maximization"
    - "strategic classification from revealed preferences"
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "inducing approximately optimal flow using truthful mediators"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-2 (induced by source paper "scalable and jointly differentially private packing"):
  with members:
    - "near optimal jointly private packing algorithms via dual multiplicative weight update"
    - "differentially private contextual linear bandits"
    - "private matchings and allocations"
    - "the possibilities and limitations of private prediction markets"
    - "privacy odometers and filters pay as you go composition"
    - "jointly private convex programming"
    - "truthful linear regression"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-3 (induced by source paper "near optimal jointly private packing algorithms via dual multiplicative weight update"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "inducing approximately optimal flow using truthful mediators"
    - "privately solving linear programs"
    - "fast algorithms for online stochastic convex programming"
    - "buying private data without verification"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-4 (induced by source paper "jointly private convex programming"):
  with members:
    - "private matchings and allocations"
    - "privately solving linear programs"
    - "differentially private empirical risk minimization efficient algorithms and tight error bounds"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "differential privacy for the analyst via private equilibrium computation"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"

Connected through hyperedge HE-5 (induced by source paper "private matchings and allocations"):
  with members:
    - "differential privacy for the analyst via private equilibrium computation"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-6 (induced by source paper "private and truthful aggregative game for large scale spectrum sharing"):
  with members:
    - "private matchings and allocations"
    - "distributed spectrum access with spatial reuse"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-7 (induced by source paper "coordination complexity small information coordinating large populations"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "learning and efficiency in games with dynamic population"
    - "welfare maximization with limited interaction"
    - "inducing approximately optimal flow using truthful mediators"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "economic efficiency requires interaction"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (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"
    - "bayesian games and the smoothness framework"
    - "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"

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)