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 "network of bandits"):
  Abstract: The distribution of machine learning tasks on the user’s devices offers several advantages for application purposes: scalability, reduction of deployment costs and privacy. We propose a basic brick, DISTRIBUTED MEDIAN ELIMINATION, which can be used to distribute the best arm identification task in various schemes. In comparison to MEDIAN ELIMINATION run on a single player, we showed a near optimal speed-up factor in O(α.N/K lnK), where K is the number of actions, N is the number of players, and α ∈ (0, 1/2]. This speed-up factor is reached with a near optimal communication cost. Experiments illustrate and complete the analysis: in comparison to MEDIAN ELIMINATION with unconstrained communication cost, the distributed version shows practical improvements.

Includes members:
  Member "best arm identification in linear bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polynomial time algorithms for multiple arm identification with full bandit feedback")
  Member "distributed exploration in multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "distributed non stochastic experts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "efficient optimal learning for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")

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.AI(Artificial Intelligence)