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 "adversarial learning for revenue maximizing auctions"):
  Abstract: We introduce a new numerical framework to learn optimal bidding strategies in repeated auctions when the seller uses past bids to optimize her mechanism. Crucially, we do not assume that the bidders know what optimization mechanism is used by the seller. We recover essentially all state-of-the-art analytical results for the single-item framework derived previously in the setup where the bidder knows the optimization mechanism used by the seller and extend our approach to multi-item settings, in which no optimal shading strategies were previously known. Our approach yields substantial increases in bidder utility in all settings. Our approach also has a strong potential for practical usage since it provides a simple way to optimize bidding strategies on modern marketplaces where buyers face unknown data-driven 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 "learning to bid in revenue maximizing auctions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions")
  Member "lp based approximation for personalized reserve prices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incentive aware contextual pricing with non parametric market noise")
  Member "learning to clear the market":
    also connected through other hyperedges:
      (none)
  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-5 (induced by source paper "learning to bid in revenue maximizing auctions")
  Member "a general theory of sample complexity for multi item profit maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "limitations of incentive compatibility on discrete type spaces")
  Member "revenue optimization with approximate bid predictions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online metric algorithms with untrusted predictions")

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)