human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the sample complexity of revenue maximization"):
  Abstract: In the design and analysis of revenue-maximizing auctions, auction performance is typically measured with respect to a prior distribution over inputs. The most obvious source for such a distribution is past data. The goal is to understand how much data is necessary and sufficient to guarantee near-optimal expected revenue. #R##N#Our basic model is a single-item auction in which bidders' valuations are drawn independently from unknown and non-identical distributions. The seller is given $m$ samples from each of these distributions "for free" and chooses an auction to run on a fresh sample. How large does m need to be, as a function of the number k of bidders and eps > 0, so that a (1 - eps)-approximation of the optimal revenue is achievable? #R##N#We prove that, under standard tail conditions on the underlying distributions, m = poly(k, 1/eps) samples are necessary and sufficient. Our lower bound stands in contrast to many recent results on simple and prior-independent auctions and fundamentally involves the interplay between bidder competition, non-identical distributions, and a very close (but still constant) approximation of the optimal revenue. It effectively shows that the only way to achieve a sufficiently good constant approximation of the optimal revenue is through a detailed understanding of bidders' valuation distributions. Our upper bound is constructive and applies in particular to a variant of the empirical Myerson auction, the natural auction that runs the revenue-maximizing auction with respect to the empirical distributions of the samples. #R##N#Our sample complexity lower bound depends on the set of allowable distributions, and to capture this we introduce alpha-strongly regular distributions, which interpolate between the well-studied classes of regular (alpha = 0) and MHR (alpha = 1) distributions. We give evidence that this definition is of independent interest.

Includes members:
  Member "the sample complexity of auctions with side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "optimal auctions for correlated buyers with sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids")
  Member "sampling and representation complexity of revenue maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "making the most of your samples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "mechanism design for data science":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimating approximate incentive compatibility")
  Member "prophet inequalities with limited information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "learning theory and algorithms for revenue optimization in second price auctions with reserve":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "mechanism design via consensus estimates cross checking and profit extraction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ironing in the dark")

Question: Please tell me which class the source paper 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)