human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "adaptive shortest path routing under unknown and stochastically varying link states"
  Abstract: We consider the adaptive shortest-path routing problem in wireless networks under unknown and stochastically varying link states. In this problem, we aim to optimize the quality of communication between a source and a destination through adaptive path selection. Due to the randomness and uncertainties in the network dynamics, the quality of each link varies over time according to a stochastic process with unknown distributions. After a path is selected for communication, the aggregated quality of all links on this path (e.g., total path delay) is observed. The quality of each individual link is not observable. We formulate this problem as a multi-armed bandit with dependent arms. We show that by exploiting arm dependencies, a regret polynomial with network size can be achieved while maintaining the optimal logarithmic order with time. This is in sharp contrast with the exponential regret order with network size offered by a direct application of the classic MAB policies that ignore arm dependencies. Furthermore, our results are obtained under a general model of link-quality distributions (including heavy-tailed distributions) and find applications in cognitive radio and ad hoc networks with unknown and dynamic communication environments.

Connected through hyperedge HE-1 (induced by source paper "learning to route efficiently with end to end feedback the value of networked structure"):
  with members:
    - "introduction to multi armed bandits"
    - "tight bounds for bandit combinatorial optimization"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "stochastic online shortest path routing the value of feedback"
    - "learning to optimize via posterior sampling"
    - "volumetric spanners an efficient exploration basis for learning"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-2 (induced by source paper "multi armed bandits on partially revealed unit interval graphs"):
  with members:
    - "unimodal bandits regret lower bounds and optimal algorithms"
    - "further optimal regret bounds for thompson sampling"
    - "leveraging side observations in stochastic bandits"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-3 (induced by source paper "near optimal adaptive shortest path routing with stochastic links states under adversarial attack"):
  with members:
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "optimal distributed online prediction using mini batches"
    - "the on line shortest path problem under partial monitoring"

Connected through hyperedge HE-4 (induced by source paper "combinatorial multi armed bandit and its extension to probabilistically triggered arms"):
  with members:
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "from bandits to experts on the value of side observations"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-5 (induced by source paper "stochastic online shortest path routing the value of feedback"):
  with members:
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "unimodal bandits regret lower bounds and optimal algorithms"
    - "matroid bandits fast combinatorial optimization with learning"
    - "online shortest path routing the value of information"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "regret in online combinatorial optimization"
    - "optimism in reinforcement learning and kullback leibler divergence"
    - "the on line shortest path problem under partial monitoring"

Connected through hyperedge HE-6 (induced by source paper "online shortest path routing the value of information"):
  with members:
    - "the on line shortest path problem under partial monitoring"

Question: Please tell me which class the center node 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)