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 "low regret bounds for bandits with knapsacks"):
  Abstract: Achievable regret bounds for Multi-Armed Bandit problems are now well-documented. They can be classified into two categories based on the dependence on the time horizon $T$: (1) small, distribution-dependent, bounds of order of magnitude $\ln(T)$ and (2) robust, distribution-free, bounds of order of magnitude $\sqrt{T}$. The Bandits with Knapsacks theory, an extension to the framework allowing to model resource consumption, lacks this duality. While several algorithms have been shown to yield asymptotically optimal distribution-free bounds on regret, there has been little progress toward the development of small distribution-dependent regret bounds. We partially bridge the gap by designing a general purpose algorithm which we show enjoy asymptotically optimal regret bounds in several cases that encompass many practical applications including dynamic pricing with limited supply and online bidding in ad auctions.

Includes members:
  Member "bandits with concave rewards and convex knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploration exploitation in constrained mdps")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploration exploitation in constrained mdps")
  Member "knapsack based optimal policies for budget limited multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advances in bandits with knapsacks")
  Member "dynamic pricing with limited supply":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "advances in bandits with knapsacks")
  Member "analysis of thompson sampling for the multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective diversity in population based reinforcement learning")

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.DS(Data Structures and Algorithms)