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 "online learning in repeated auctions"):
  Abstract: Motivated by online advertising auctions, we consider repeated Vickrey auctions where goods of unknown value are sold sequentially and bidders only learn (potentially noisy) information about a good's value once it is purchased. We adopt an online learning approach with bandit feedback to model this problem and derive bidding strategies for two models: stochastic and adversarial. In the stochastic model, the observed values of the goods are random variables centered around the true value of the good. In this case, logarithmic regret is achievable when competing against well behaved adversaries. In the adversarial model, the goods need not be identical and we simply compare our performance against that of the best fixed bid in hindsight. We show that sublinear regret is also achievable in this case and prove matching minimax lower bounds. To our knowledge, this is the first complete set of strategies for bidders participating in auctions of this type.

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 "the sample complexity of revenue maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "adaptive online learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive online learning with varying norms")
  Member "optimal auctions for correlated buyers with sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids")
  Member "online learning and profit maximization from revealed preferences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "mechanism design for data science":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimating approximate incentive compatibility")
  Member "learning valuation distributions from partial observation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "heavy tails make happy buyers")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploration exploitation in constrained mdps")

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)