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 "learning to bid in revenue maximizing auctions"):
  Abstract: We consider the problem of the optimization of bidding strategies in prior-dependent revenue-maximizing auctions, when the seller fixes the reserve prices based on the bid distributions. Our study is done in the setting where one bidder is strategic. Using a variational approach, we study the complexity of the original objective and we introduce a relaxation of the objective functional in order to use gradient descent methods. Our approach is simple, general and can be applied to various value distributions and revenue-maximizing mechanisms. The new strategies we derive yield massive uplifts compared to the traditional truthfully bidding strategy.

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 "explicit shading strategies for repeated truthful auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions")
  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 "robust stackelberg buyers in repeated auctions")
  Member "learning to bid without knowing your value":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks")
  Member "optimal auctions through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bittensor an intermodel intelligence measure")
  Member "a field guide to personalized reserve prices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "reserve pricing in repeated second price auctions with strategic 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)