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 "stochastic online shortest path routing the value of feedback"):
  Abstract: This paper studies online shortest path routing over multi-hop networks. Link costs or delays are time-varying and modeled by independent and identically distributed random processes, whose parameters are initially unknown. The parameters, and hence the optimal path, can only be estimated by routing packets through the network and observing the realized delays. Our aim is to find a routing policy that minimizes the regret (the cumulative difference of expected delay) between the path chosen by the policy and the unknown optimal path. We formulate the problem as a combinatorial bandit optimization problem and consider several scenarios that differ in where routing decisions are made and in the information available when making the decisions. For each scenario, we derive a tight asymptotic lower bound on the regret that has to be satisfied by any online routing policy. These bounds help us to understand the performance improvements we can expect when (i) taking routing decisions at each hop rather than at the source only, and (ii) observing per-link delays rather than end-to-end path delays. In particular, we show that (i) is of no use while (ii) can have a spectacular impact. Three algorithms, with a trade-off between computational complexity and performance, are proposed. The regret upper bounds of these algorithms improve over those of the existing algorithms, and they significantly outperform state-of-the-art algorithms in numerical experiments.

Includes members:
  Member "tight regret bounds for stochastic combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "unimodal bandits regret lower bounds and optimal algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "closed form whittle s index enabled random access for timely status update")
  Member "matroid bandits fast combinatorial optimization with learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "online shortest path routing the value of information":
    also connected through other hyperedges:
      (none)
  Member "adaptive shortest path routing under unknown and stochastically varying link states":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to route efficiently with end to end feedback the value of networked structure")
  Member "towards minimax policies for online linear optimization with bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regret bounds for batched bandits")
  Member "regret in online combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "optimism in reinforcement learning and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "accelerated learning from recommender systems using multi armed bandit")

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.NI(Networking and Internet Architecture)