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 "learning to route efficiently with end to end feedback the value of networked structure"):
  Abstract: We introduce efficient algorithms which achieve nearly optimal regrets for the problem of stochastic online shortest path routing with end-to-end feedback. The setting is a natural application of the combinatorial stochastic bandits problem, a special case of the linear stochastic bandits problem. We show how the difficulties posed by the large scale action set can be overcome by the networked structure of the action set. Our approach presents a novel connection between bandit learning and shortest path algorithms. Our main contribution is an adaptive exploration algorithm with nearly optimal instance-dependent regret for any directed acyclic network. We then modify it so that nearly optimal worst case regret is achieved simultaneously. Driven by the carefully designed Top-Two Comparison (TTC) technique, the algorithms are efficiently implementable. We further conduct extensive numerical experiments to show that our proposed algorithms not only achieve superior regret performances, but also reduce the runtime drastically.

Includes members:
  Member "introduction to multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "tight bounds for bandit combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "top k combinatorial bandits with full bandit feedback")
  Member "tight regret bounds for stochastic combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "stochastic online shortest path routing the value of feedback":
    also connected through other hyperedges:
      (none)
  Member "learning to optimize via posterior sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "volumetric spanners an efficient exploration basis for learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explore no more improved high probability regret bounds for non stochastic bandits")
  Member "adaptive shortest path routing under unknown and stochastically varying link states":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi armed bandits on partially revealed unit interval graphs")
  Member "thompson sampling for contextual bandits with linear payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural contextual bandits with ucb based exploration")

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)