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 "bulow klemperer style results for welfare maximization in two sided markets"):
  Abstract: We consider the problem of welfare maximization in two-sided markets using simple mechanisms that are prior-independent. The seminal impossibility result of Myerson and Satterthwaite [1983] shows that even for bilateral trade, there is no feasible (IR, truthful and budget balanced) mechanism that has welfare as high as the optimal-yet-infeasible VCG mechanism, which attains maximal welfare but runs a deficit. On the other hand, the optimal feasible mechanism needs to be carefully tailored to the Bayesian prior, and is extremely complex, eluding a precise description. #R##N#In this paper we present Bulow-Klemperer-style results to circumvent these hurdles in double-auction market settings. We suggest using the Buyer Trade Reduction (BTR) mechanism, a variant of McAfee's mechanism, that is feasible and simple (in particular, it is deterministic, prior-independent, and anonymous). First, in the setting where the buyers' and sellers' values are sampled i.i.d. from the same distribution, we show that for any such market of any size, BTR with one additional buyer whose value is sampled from the same distribution has expected welfare at least as high as the optimal in the original market. #R##N#We then move to a more general setting where the buyers' values are sampled from one distribution, and the sellers' from another, focusing on the case where the buyers' distribution first-order stochastically dominates the sellers' distribution. We present bounds on the number of buyers that, when added, cause BTR in the augmented market to achieve welfare at least as high as the optimal in the original market. Our lower bounds extend to a large class of mechanisms. In addition, we present positive results about the usefulness of pricing at a sample for welfare maximization in two-sided markets under the above two settings, which to the best of our knowledge are the first sampling results in this context.

Includes members:
  Member "the vickrey auction with a single duplicate bidder approximates the optimal revenue":
    also connected through other hyperedges:
      (none)
  Member "99 revenue via enhanced competition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the vickrey auction with a single duplicate bidder approximates the optimal revenue")
  Member "the best of both worlds asymptotically efficient mechanisms with a guarantee on the expected gains from trade":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient two sided markets with limited information")
  Member "are two samples really better than one on the non asymptotic performance of empirical revenue maximization":
    also connected through other hyperedges:
      (none)
  Member "optimal and benchmark optimal competition complexity for additive buyers over independent items":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "approximating gains from trade in two sided markets via simple mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient two sided markets with limited information")
  Member "submultiplicative glivenko cantelli and uniform convergence of revenues":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a general theory of sample complexity for multi item profit maximization")
  Member "a general theory of sample complexity for multi item profit maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "limitations of incentive compatibility on discrete type spaces")

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)