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: "prior independent auctions for risk averse agents"
  Abstract: We study simple and approximately optimal auctions for agents with a particular form of risk-averse preferences. We show that, for symmetric agents, the optimal revenue (given a prior distribution over the agent preferences) can be approximated by the first-price auction (which is prior independent), and, for asymmetric agents, the optimal revenue can be approximated by an auction with simple form. These results are based on two technical methods. The first is for upper-bounding the revenue from a risk-averse agent. The second gives a payment identity for mechanisms with pay-your-bid semantics.

Connected through hyperedge HE-1 (induced by source paper "optimal auctions vs anonymous pricing beyond linear utility"):
  with members:
    - "tight revenue gaps among simple mechanisms"
    - "tight approximation ratio of anonymous pricing"
    - "optimal auctions vs anonymous pricing"
    - "bayesian optimal auctions via multi to single agent reduction"
    - "the simple economics of approximately optimal auctions"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "sequential posted pricing and multi parameter mechanism design"
    - "mechanism design via correlation gap"

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"
    - "price of anarchy for auction revenue"
    - "efficiency guarantees in auctions with budgets"

Connected through hyperedge HE-3 (induced by source paper "explicit shading strategies for repeated truthful auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "how to manipulate truthful prior dependent mechanisms"
    - "the sample complexity of revenue maximization"
    - "online learning in repeated auctions"
    - "learning in auctions regret is hard envy is easy"
    - "learning valuation distributions from partial observation"

Connected through hyperedge HE-4 (induced by source paper "optimal mechanism design with risk loving agents"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "revenue maximization with an uncertainty averse buyer"
    - "energy coupon a mean field game perspective on demand response in smart grids"
    - "selling two goods optimally"
    - "duality and optimality of auctions for uniform distributions"
    - "strong duality for a multiple good monopolist"
    - "mechanism design via optimal transport"
    - "mechanisms for risk averse agents without loss"

Connected through hyperedge HE-5 (induced by source paper "prior independent equilibria and linear multi dimensional bayesian games"):
  with members:
    - "network cournot competition"
    - "graphical models for game theory"
    - "multi games and a double game extension of the prisoner s dilemma"
    - "the robust price of anarchy of altruistic games"
    - "price of anarchy for greedy auctions"

Connected through hyperedge HE-6 (induced by source paper "revenue maximization with an uncertainty averse buyer"):
  with members:
    - "on the complexity of dynamic mechanism design"

Connected through hyperedge HE-7 (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-8 (induced by source paper "how to manipulate truthful prior dependent mechanisms"):
  with members:
    - "randomization beats second price as a prior independent auction"
    - "perfect bayesian equilibria in repeated sales"
    - "an algorithmic characterization of multi dimensional mechanisms"

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)