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 "explicit shading strategies for repeated truthful auctions"):
  Abstract: With the increasing use of auctions in online advertising, there has been a large effort to study seller revenue maximization, following Myerson's seminal work, both theoretically and practically. We take the point of view of the buyer in classical auctions and ask the question of whether she has an incentive to shade her bid even in auctions that are reputed to be truthful, when aware of the revenue optimization mechanism. #R##N#We show that in auctions such as the Myerson auction or a VCG with reserve price set as the monopoly price, the buyer who is aware of this information has indeed an incentive to shade. Intuitively, by selecting the revenue maximizing auction, the seller introduces a dependency on the buyers' distributions in the choice of the auction. We study in depth the case of the Myerson auction and show that a symmetric equilibrium exists in which buyers shade non-linearly what would be their first price bid. They then end up with an expected payoff that is equal to what they would get in a first price auction with no reserve price. #R##N#We conclude that a return to simple first price auctions with no reserve price or at least non-dynamic anonymous ones is desirable from the point of view of both buyers, sellers and increasing transparency.

Includes members:
  Member "dynamic reserve prices for repeated auctions learning from bids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions")
  Member "a field guide to personalized reserve prices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions")
  Member "learning simple auctions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "how to manipulate truthful prior dependent mechanisms":
    also connected through other hyperedges:
      (none)
  Member "the sample complexity of revenue maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "online learning in repeated auctions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents")
  Member "learning in auctions regret is hard envy is easy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning reserve prices in second price auctions")
  Member "learning valuation distributions from partial observation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "heavy tails make happy buyers")

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)