human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sequential mechanisms with ex post participation guarantees"
  Abstract: We provide a characterization of revenue-optimal dynamic mechanisms in settings where a monopolist sells k items over k periods to a buyer who realizes his value for item i in the beginning of period i. We require that the mechanism satisfies a strong individual rationality constraint, requiring that the stage utility of each agent be positive during each period. We show that the optimum mechanism can be computed by solving a nested sequence of static (single-period) mechanisms that optimize a tradeoff between the surplus of the allocation and the buyer's utility. We also provide a simple dynamic mechanism that obtains at least half of the optimal revenue. The mechanism either ignores history and posts the optimal monopoly price in each period, or allocates with a probability that is independent of the current report of the agent and is based only on previous reports. Our characterization extends to multi-agent auctions. We also formulate a discounted infinite horizon version of the problem, where we study the performance of "Markov mechanisms."

Connected through hyperedge HE-1 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "optimal pricing in repeated posted price auctions"
    - "learning optimal reserve price against non myopic bidders"
    - "contextual search via intrinsic volumes"
    - "robust repeated auctions under heterogeneous buyer behavior"
    - "optimal dynamic auctions are virtual welfare maximizers"
    - "dynamic pricing with finitely many unknown valuations"
    - "on consistency of optimal pricing algorithms in repeated posted price auctions with strategic buyer"

Connected through hyperedge HE-2 (induced by source paper "adversarial learning for revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "learning to bid in revenue maximizing auctions"
    - "lp based approximation for personalized reserve prices"
    - "learning to clear the market"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "a general theory of sample complexity for multi item profit maximization"
    - "revenue optimization with approximate bid predictions"

Connected through hyperedge HE-3 (induced by source paper "learning to bid in revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "explicit shading strategies for repeated truthful auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
    - "learning to bid without knowing your value"
    - "optimal auctions through deep learning"
    - "a field guide to personalized reserve prices"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-4 (induced by source paper "robust stackelberg buyers in repeated auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
    - "optimal auctions through deep learning"
    - "a field guide to personalized reserve prices"
    - "the sample complexity of revenue maximization"
    - "making the most of your samples"

Connected through hyperedge HE-5 (induced by source paper "dynamic first price auctions robust to heterogeneous buyers"):
  with members:
    - "robust repeated auctions under heterogeneous buyer behavior"
    - "selling to a no regret buyer"
    - "on the complexity of dynamic mechanism design"
    - "perfect bayesian equilibria in repeated sales"
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-6 (induced by source paper "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "learning to bid in revenue maximizing auctions"
    - "online learning for measuring incentive compatibility in ad auctions"
    - "explicit shading strategies for repeated truthful auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-7 (induced by source paper "selling to a no regret buyer"):
  with members:
    - "learning multi item auctions with or without samples"
    - "on the competition complexity of dynamic mechanism design"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "optimal dynamic mechanisms with ex post ir via bank accounts"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "the pseudo dimension of near optimal auctions"

Connected through hyperedge HE-8 (induced by source paper "on consistency of optimal pricing algorithms in repeated posted price auctions with strategic buyer"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "non parametric revenue optimization for generalized second price auctions"
    - "online learning in repeated auctions"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "dynamic pricing with limited supply"
    - "sequential auctions and externalities"
    - "general auction mechanism for search advertising"

Question: Please tell me which class the center node 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)