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: "optimal auctions for correlated buyers with sampling"
  Abstract: Cr\'emer and McLean [1985] showed that, when buyers' valuations are drawn from a correlated distribution, an auction with full knowledge on the distribution can extract the full social surplus. We study whether this phenomenon persists when the auctioneer has only incomplete knowledge of the distribution, represented by a finite family of candidate distributions, and has sample access to the real distribution. We show that the naive approach which uses samples to distinguish candidate distributions may fail, whereas an extended version of the Cr\'emer-McLean auction simultaneously extracts full social surplus under each candidate distribution. With an algebraic argument, we give a tight bound on the number of samples needed by this auction, which is the difference between the number of candidate distributions and the dimension of the linear space they span.

Connected through hyperedge HE-1 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "making the most of your samples"
    - "perfect bayesian equilibria in repeated sales"
    - "revenue optimization in posted price auctions with strategic buyers"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "efficient nash computation in large population games with bounded influence"

Connected through hyperedge HE-2 (induced by source paper "a b testing of auctions"):
  with members:
    - "the sample complexity of revenue maximization"
    - "mechanism design for data science"

Connected through hyperedge HE-3 (induced by source paper "online learning in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "the sample complexity of revenue maximization"
    - "adaptive online learning"
    - "online learning and profit maximization from revealed preferences"
    - "mechanism design for data science"
    - "learning valuation distributions from partial observation"
    - "bandits with knapsacks"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-4 (induced by source paper "ironing in the dark"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the pseudo dimension of near optimal auctions"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "mechanism design for data science"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "vickrey auctions for irregular distributions"
    - "envy freedom and prior free mechanism design"

Connected through hyperedge HE-5 (induced by source paper "the sample complexity of revenue maximization"):
  with members:
    - "the sample complexity of auctions with side information"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "mechanism design for data science"
    - "prophet inequalities with limited information"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "mechanism design via consensus estimates cross checking and profit extraction"
    - "sequential posted pricing and multi parameter mechanism design"

Connected through hyperedge HE-6 (induced by source paper "randomization beats second price as a prior independent auction"):
  with members:
    - "the sample complexity of revenue maximization"
    - "making the most of your samples"
    - "prior independent auctions for risk averse agents"

Connected through hyperedge HE-7 (induced by source paper "making the most of your samples"):
  with members:
    - "the sample complexity of revenue maximization"
    - "optimal platform design"
    - "mechanism design for data science"
    - "vickrey auctions for irregular distributions"

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)