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: "price of anarchy for auction revenue"
  Abstract: This paper develops tools for welfare and revenue analyses of Bayes-Nash equilibria in asymmetric auctions with single-dimensional agents. We employ these tools to derive price of anarchy results for social welfare and revenue. Our approach separates the standard smoothness framework into two distinct parts, isolating the analysis common to any auction from the analysis specific to a given auction. The first part relates a bidder's contribution to welfare in equilibrium to their contribution to welfare in the optimal auction using the price the bidder faces for additional allocation. Intuitively, either an agent's utility and hence contribution to welfare is high, or the price she has to pay for additional allocation is high relative to her value. We call this condition value covering; it holds in every Bayes-Nash equilibrium of any auction. The second part, revenue covering, relates the prices bidders face for additional allocation to the revenue of the auction, using an auction's rules and feasibility constraints. Combining the two parts gives approximation results to the optimal welfare, and, under the right conditions, the optimal revenue. In mechanisms with reserve prices, our welfare results show approximation with respect to the optimal mechanism with the same reserves. #R##N#As a center-piece result, we analyze the single-item first-price auction with individual monopoly reserves. When each distribution satisfies a regularity condition the auction's revenue is at least a $2e/(e-1) \approx 3.16$ approximation to the revenue of the optimal auction. We also give bounds for matroid auctions with first-price or all-pay semantics, and the generalized first-price position auction. Finally, we give an extension theorem for simultaneous composition, i.e., when multiple auctions are run simultaneously, with single-valued, unit-demand agents.

Connected through hyperedge HE-1 (induced by source paper "simple credible and approximately optimal auctions"):
  with members:
    - "an improved welfare guarantee for first price auctions"
    - "learning multi item auctions with or without samples"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "sample complexity for non truthful mechanisms"
    - "the price of anarchy in auctions"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"

Connected through hyperedge HE-2 (induced by source paper "an end to end argument in mechanism design prior independent auctions for budgeted agents"):
  with members:
    - "a b testing of auctions"
    - "the price of anarchy in auctions"
    - "randomization beats second price as a prior independent auction"
    - "approximate revenue maximization in interdependent value settings"
    - "mechanism design for data science"
    - "simple and near optimal mechanisms for market intermediation"
    - "efficiency guarantees in auctions with budgets"
    - "prior independent mechanisms for scheduling"

Connected through hyperedge HE-3 (induced by source paper "an improved welfare guarantee for first price auctions"):
  with members:
    - "the price of anarchy in auctions"
    - "on the efficiency of all pay mechanisms"
    - "no regret learning in bayesian games"
    - "tight bounds for the price of anarchy of simultaneous first price auctions"
    - "composable and efficient mechanisms"
    - "price of anarchy for greedy auctions"

Connected through hyperedge HE-4 (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-5 (induced by source paper "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"):
  with members:
    - "makespan minimization via posted prices"
    - "simple mechanisms for subadditive buyers via duality"
    - "combinatorial prophet inequalities"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "the price of anarchy in large games"
    - "online contention resolution schemes"
    - "the invisible hand of dynamic market pricing"
    - "algorithms as mechanisms the price of anarchy of relax and round"

Connected through hyperedge HE-6 (induced by source paper "sample complexity for non truthful mechanisms"):
  with members:
    - "an end to end argument in mechanism design prior independent auctions for budgeted agents"
    - "bernoulli factories and black box reductions in mechanism design"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "the price of anarchy in auctions"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "non optimal mechanism design"

Connected through hyperedge HE-7 (induced by source paper "descending price optimally coordinates search"):
  with members:
    - "optimal coordinated planning amongst self interested agents with private state"
    - "price of anarchy for greedy auctions"

Connected through hyperedge HE-8 (induced by source paper "descending price coordinates approximately efficient search"):
  with members:
    - "bayesian games and the smoothness framework"
    - "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)