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 "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.

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 "robust repeated auctions under heterogeneous buyer behavior":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders")
  Member "selling to a no regret buyer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "sequential mechanisms with ex post participation guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders")
  Member "on the complexity of dynamic mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic first price auctions robust to heterogeneous buyers")
  Member "learning prices for repeated auctions with strategic buyers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids")

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)