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 "are two samples really better than one on the non asymptotic performance of empirical revenue maximization"):
  Abstract: The literature on "mechanism design from samples," which has flourished in recent years at the interface of economics and computer science, offers a bridge between the classic computer-science approach of worst-case analysis (corresponding to "no samples") and the classic economic approach of average-case analysis for a given Bayesian prior (conceptually corresponding to the number of samples tending to infinity). Nonetheless, the two directions studied so far are two extreme and almost diametrically opposed directions: that of asymptotic results where the number of samples grows large, and that where only a single sample is available. In this paper, we take a first step toward understanding the middle ground that bridges these two approaches: that of a fixed number of samples greater than one. In a variety of contexts, we ask what is possibly the most fundamental question in this direction: "are two samples really better than one sample?". We present a few surprising negative result, and complement them with our main result: showing that the worst-case, over all regular distributions, expected-revenue guarantee of the Empirical Revenue Maximization algorithm given two samples is greater than that of this algorithm given one sample. The proof is technically challenging, and provides the first result that shows that some deterministic mechanism constructed using two samples can guarantee more than one half of the optimal revenue.

Includes members:
  Member "a general theory of sample complexity for multi item profit maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limitations of incentive compatibility on discrete type spaces")
  Member "learning simple auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "efficient empirical revenue maximization in single parameter auction environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "sample complexity of automated mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning reserve prices in second price auctions")
  Member "the sample complexity of revenue maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "randomization beats second price as a prior independent auction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmark design and prior independent optimization")
  Member "online learning in repeated auctions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents")
  Member "making the most of your samples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a permutation equivariant neural network architecture for auction design")

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)