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 "best response dynamics in combinatorial auctions with item bidding"):
  Abstract: In a combinatorial auction with item bidding, agents participate in multiple single-item second-price auctions at once. As some items might be substitutes, agents need to strategize in order to maximize their utilities. A number of results indicate that high welfare can be achieved this way, giving bounds on the welfare at equilibrium. Recently, however, criticism has been raised that equilibria are hard to compute and therefore unlikely to be attained. #R##N#In this paper, we take a different perspective. We study simple best-response dynamics. That is, agents are activated one after the other and each activated agent updates his strategy myopically to a best response against the other agents' current strategies. Often these dynamics may take exponentially long before they converge or they may not converge at all. However, as we show, convergence is not even necessary for good welfare guarantees. Given that agents' bid updates are aggressive enough but not too aggressive, the game will remain in states of good welfare after each agent has updated his bid at least once. #R##N#In more detail, we show that if agents have fractionally subadditive valuations, natural dynamics reach and remain in a state that provides a $1/3$ approximation to the optimal welfare after each agent has updated his bid at least once. For subadditive valuations, we can guarantee an $\Omega(1/\log m)$ approximation in case of $m$ items that applies after each agent has updated his bid at least once and at any point after that. The latter bound is complemented by a negative result, showing that no kind of best-response dynamics can guarantee more than an $o(\log \log m/\log m)$ fraction of the optimal social welfare.

Includes members:
  Member "breaking the logarithmic barrier for truthful combinatorial auctions with submodular bidders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved truthful mechanisms for combinatorial auctions with submodular bidders")
  Member "interpolating between truthful and non truthful mechanisms for combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on simultaneous two player combinatorial auctions")
  Member "learning in auctions regret is hard envy is easy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning reserve prices in second price auctions")
  Member "on the complexity of computing an equilibrium in combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on simultaneous two player combinatorial auctions")
  Member "a unifying hierarchy of valuations with complements and substitutes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond submodular maximization")
  Member "valuation compressions in vcg based combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs")
  Member "non price equilibria in markets of discrete goods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "complexity theory game theory and economics the barbados lectures")

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)