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 "a bridge between liquid and social welfare in combinatorial auctions with submodular bidders"):
  Abstract: We study incentive compatible mechanisms for Combinatorial Auctions where the bidders have submodular (or XOS) valuations and are budget-constrained. Our objective is to maximize the \emph{liquid welfare}, a notion of efficiency for budget-constrained bidders introduced by Dobzinski and Paes Leme (2014). We show that some of the known truthful mechanisms that best-approximate the social welfare for Combinatorial Auctions with submodular bidders through demand query oracles can be adapted, so that they retain truthfulness and achieve asymptotically the same approximation guarantees for the liquid welfare. More specifically, for the problem of optimizing the liquid welfare in Combinatorial Auctions with submodular bidders, we obtain a universally truthful randomized $O(\log m)$-approximate mechanism, where $m$ is the number of items, by adapting the mechanism of Krysta and Vocking (2012). #R##N#Additionally, motivated by large market assumptions often used in mechanism design, we introduce a notion of competitive markets and show that in such markets, liquid welfare can be approximated within a constant factor by a randomized universally truthful mechanism. Finally, in the Bayesian setting, we obtain a truthful $O(1)$-approximate mechanism for the case where bidder valuations are generated as independent samples from a known distribution, by adapting the results of Feldman, Gravin and Lucier (2014).

Includes members:
  Member "a bayesian clearing mechanism for combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "machine learning powered iterative combinatorial auctions")
  Member "best response dynamics in combinatorial auctions with item bidding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "proportional dynamics in exchange economies")
  Member "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the power and limits of dynamic pricing in combinatorial markets")
  Member "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved truthful mechanisms for combinatorial auctions with submodular bidders")
  Member "bayesian budget feasibility with posted pricing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "combinatorial auctions via posted prices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "budget feasible mechanism design from prior free to bayesian":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "from convex optimization to randomized mechanisms toward optimal combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved truthful mechanisms for combinatorial auctions with submodular bidders")

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)