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 "dynamic pricing with finitely many unknown valuations"):
  Abstract: Motivated by posted price auctions where buyers are grouped in an unknown number of latent types characterized by their private values for the good on sale, we investigate revenue maximization in stochastic dynamic pricing when the distribution of buyers' private values is supported on an unknown set of points in [0,1] of unknown cardinality K. This setting can be viewed as an instance of a stochastic K-armed bandit problem where the location of the arms (the K unknown valuations) must be learned as well. In the distribution-free case, we show that our setting is just as hard as K-armed stochastic bandits: we prove that no algorithm can achieve a regret significantly better than $\sqrt{KT}$, (where T is the time horizon) and present an efficient algorithm matching this lower bound up to logarithmic factors. In the distribution-dependent case, we show that for all K>2 our setting is strictly harder than K-armed stochastic bandits by proving that it is impossible to obtain regret bounds that grow logarithmically in time or slower. On the other hand, when a lower bound $\gamma>0$ on the smallest drop in the demand curve is known, we prove an upper bound on the regret of order $(1/\Delta+(\log \log T)/\gamma^2)(K\log T)$. This is a significant improvement on previously known regret bounds for discontinuous demand curves, that are at best of order $(K^{12}/\gamma^8)\sqrt{T}$. When K=2 in the distribution-dependent case, the hardness of our setting reduces to that of a stochastic 2-armed bandit: we prove that an upper bound of order $(\log T)/\Delta$ (up to $\log\log$ factors) on the regret can be achieved with no information on the demand curve. Finally, we show a $O(\sqrt{T})$ upper bound on the regret for the setting in which the buyers' decisions are nonstochastic, and the regret is measured with respect to the best between two fixed valuations one of which is known to the seller.

Includes members:
  Member "fast rates for bandit optimization with upper confidence frank wolfe":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "selfish robustness and equilibria in multi player bandits")
  Member "online learning in repeated auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents")
  Member "bandits with concave rewards and convex knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration exploitation in constrained mdps")
  Member "perfect bayesian equilibria in repeated sales":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploration exploitation in constrained mdps")
  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")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "dynamic pricing with limited supply":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "advances in bandits with knapsacks")

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.LG(Machine Learning)