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: "perfect bayesian equilibria in repeated sales"
  Abstract: A special case of Myerson's classic result describes the revenue-optimal equilibrium when a seller offers a single item to a buyer. We study a repeated sales extension of this model: a seller offers to sell a single fresh copy of an item to the same buyer every day via a posted price. The buyer's value for the item is unknown to the seller but is drawn initially from a publicly known distribution F and remains the same throughout. #R##N#We study this setting where the seller is unable to commit to future prices and find several surprises. First, if the horizon is fixed, previous work showed that an equilibrium exists, and all equilibria yield tiny or constant revenue. This is a far cry from the linearly growing benchmark of getting Myerson optimal revenue each day. Our first result shows that this is because the buyer strategies in these equilibria are necessarily unnatural. We restrict to a natural class of buyer strategies called threshold strategies, and show that threshold equilibria rarely exist. Second, if the seller can commit not to raise prices upon purchase, while still retaining the possibility of lowering prices in future, we show that threshold equilibria are guaranteed to exist for the power law family of distributions. As an example, if F is uniform in [0,1], the seller can extract revenue of order $\sqrt{n}$ in $n$ rounds as opposed to the constant revenue obtainable when he is unable to make commitments. Finally, we consider the infinite horizon game with partial commitment, where both the seller and the buyer discount the future utility by a factor of $1-\delta \in [0,1)$. When the value distribution is uniform in [0,1], there exists a threshold equilibrium with expected revenue at least $\frac{4}{3+2\sqrt{2}} \sim 69$% of the Myerson optimal revenue benchmark.

Connected through hyperedge HE-1 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "optimal auctions for correlated buyers with sampling"
    - "making the most of your samples"
    - "revenue optimization in posted price auctions with strategic buyers"
    - "learning prices for repeated auctions with strategic buyers"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "efficient nash computation in large population games with bounded influence"

Connected through hyperedge HE-2 (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-3 (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"
    - "sequential mechanisms with ex post participation guarantees"
    - "on the complexity of dynamic mechanism design"
    - "online bandit learning against an adaptive adversary from regret to policy regret"

Connected through hyperedge HE-4 (induced by source paper "learning optimal reserve price against non myopic bidders"):
  with members:
    - "multi scale online learning and its applications to online auctions"
    - "the price of differential privacy for online learning"
    - "learning simple auctions"
    - "jointly private convex programming"
    - "the sample complexity of revenue maximization"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "learning prices for repeated auctions with strategic buyers"

Connected through hyperedge HE-5 (induced by source paper "dynamic pricing with finitely many unknown valuations"):
  with members:
    - "fast rates for bandit optimization with upper confidence frank wolfe"
    - "online learning in repeated auctions"
    - "bandits with concave rewards and convex knapsacks"
    - "bandits with knapsacks"
    - "learning prices for repeated auctions with strategic buyers"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"

Connected through hyperedge HE-6 (induced by source paper "new mechanism for repeated posted price auction with a strategic buyer without discounting"):
  with members:
    - "on consistency of optimal pricing algorithms in repeated posted price auctions with strategic buyer"
    - "learning prices for repeated auctions with strategic buyers"
    - "on the power of randomization in algorithmic mechanism design"

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"
    - "sequential mechanisms with ex post participation guarantees"
    - "optimal dynamic mechanisms with ex post ir via bank accounts"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-8 (induced by source paper "multi scale online learning and its applications to online auctions"):
  with members:
    - "a sample complexity measure with applications to learning optimal auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "bandits with concave rewards and convex knapsacks"
    - "making the most of your samples"
    - "bandits with knapsacks"

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)