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 "smoothed analysis of multi item auctions with correlated values"):
  Abstract: Consider a seller with m heterogeneous items for sale to a single additive buyer whose values for the items are arbitrarily correlated. It was previously shown that, in such settings, distributions exist for which the seller's optimal revenue is infinite, but the best "simple" mechanism achieves revenue at most one ([Briest et. al 15], [Hart and Nisan 13]), even when $m=2$. This result has long served as a cautionary tale discouraging the study of multi-item auctions without some notion of "independent items". #R##N#In this work we initiate a smoothed analysis of such multi-item auction settings. We consider a buyer whose item values are drawn from an arbitrarily correlated multi-dimensional distribution then randomly perturbed with magnitude $\delta$ under several natural perturbation models. On one hand, we prove that the ([Briest et. al 15], [Hart and Nisan 13]) construction is surprisingly robust to certain natural perturbations of this form, and the infinite gap remains. #R##N#On the other hand, we provide a smoothed model such that the approximation guarantee of simple mechanisms is smoothed-finite. We show that when the perturbation has magnitude $\delta$, pricing only the grand bundle guarantees an $O(1/\delta)$-approximation to the optimal revenue. That is, no matter the (worst-case) initially correlated distribution, these tiny perturbations suffice to bring the gap down from infinite to finite. We further show that the same guarantees hold when $n$ buyers have values drawn from an arbitrarily correlated $mn$-dimensional distribution (without any dependence on $n$). #R##N#Taken together, these analyses further pin down key properties of correlated distributions that result in large gaps between simplicity and optimality.

Includes members:
  Member "approximate revenue maximization with multiple items":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "the value of information concealment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "persuasion and incentives through the lens of duality")
  Member "smoothed and average case approximation ratios of mechanisms beyond the worst case analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "average case analysis of the assignment problem with independent preferences")
  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 "mechanism design for subadditive agents via an ex ante relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "simple mechanisms for a subadditive buyer and applications to revenue monotonicity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "a simple and approximately optimal mechanism for an additive buyer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "optimal competitive auctions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "benchmark design and prior independent optimization")

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)