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 "robust stackelberg buyers in repeated auctions"):
  Abstract: We consider the practical and classical setting where the seller is using an exploration stage to learn the value distributions of the bidders before running a revenue-maximizing auction in a exploitation phase. In this two-stage process, we exhibit practical, simple and robust strategies with large utility uplifts for the bidders. We quantify precisely the seller revenue against non-discounted buyers, complementing recent studies that had focused on impatient/heavily discounted buyers. We also prove the robustness of these shading strategies to sample approximation error of the seller, to bidder's approximation error of the competition and to possible change of the mechanisms.

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 "dynamic incentive aware learning robust pricing in contextual auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "protecting consumers against personalized pricing a stopping time approach")
  Member "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial learning for revenue maximizing auctions")
  Member "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to bid in revenue maximizing auctions")
  Member "optimal auctions through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bittensor an intermodel intelligence measure")
  Member "a field guide to personalized reserve prices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions")
  Member "sequential mechanisms with ex post participation guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders")
  Member "the sample complexity of revenue maximization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)