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 "an efficient e bic to bic transformation and its application to black box reduction in revenue maximization"):
  Abstract: We consider the black-box reduction from multi-dimensional revenue maximization to virtual welfare maximization. Cai et al.~\cite{CaiDW12a, CaiDW12b,CaiDW13a,CaiDW13b} show a polynomial-time approximation-preserving reduction, however, the mechanism produced by their reduction is only approximately Bayesian incentive compatible ($\varepsilon$-BIC). We provide a new polynomial time transformation that converts any $\varepsilon$-BIC mechanism to an exactly BIC mechanism with only a negligible revenue loss. Our transformation applies to a very general mechanism design setting and only requires sample access to the agents' type distributions and query access to the original $\varepsilon$-BIC mechanism. Other $\varepsilon$-BIC to BIC transformations exist in the literature~\cite{DaskalakisW12, RubinsteinW15, CaiZ17} but all require exponential time to run. As an application of our transformation, we improve the reduction by Cai et al.~\cite{CaiDW12a, CaiDW12b,CaiDW13a,CaiDW13b} to generate an exactly BIC mechanism. #R##N#Our transformation builds on a novel idea developed in a recent paper by Dughmi et al.~\cite{dughmi2017bernoulli}: finding the maximum entropy regularized matching using Bernoulli factories. The original algorithm by Dughmi et al.~\cite{dughmi2017bernoulli} can only handle graphs with nonnegative edge weights, while our transformation requires finding the maximum entropy regularized matching on graphs with both positive and negative edge weights. The main technical contribution of this paper is a new algorithm that can accommodate arbitrary edge weights.

Includes members:
  Member "multi item mechanisms without item independence learnability via robustness":
    also connected through other hyperedges:
      (none)
  Member "the complexity of black box mechanism design with priors":
    also connected through other hyperedges:
      (none)
  Member "approximation schemes for a buyer with independent items via symmetries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "a duality based unified approach to bayesian mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "bernoulli factories and black box reductions in mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the complexity of contracts")
  Member "simple mechanisms for subadditive buyers via duality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "revenue maximization and ex post budget constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a duality based unified approach to bayesian mechanism design")
  Member "mechanism design for subadditive agents via an ex ante relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)