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: "optimal rate sampling in 802 11 systems"
  Abstract: In 802.11 systems, Rate Adaptation (RA) is a fundamental mechanism allowing transmitters to adapt the coding and modulation scheme as well as the MIMO transmission mode to the radio channel conditions, and in turn, to learn and track the (mode, rate) pair providing the highest throughput. So far, the design of RA mechanisms has been mainly driven by heuristics. In contrast, in this paper, we rigorously formulate such design as an online stochastic optimisation problem. We solve this problem and present ORS (Optimal Rate Sampling), a family of (mode, rate) pair adaptation algorithms that provably learn as fast as it is possible the best pair for transmission. We study the performance of ORS algorithms in both stationary radio environments where the successful packet transmission probabilities at the various (mode, rate) pairs do not vary over time, and in non-stationary environments where these probabilities evolve. We show that under ORS algorithms, the throughput loss due to the need to explore sub-optimal (mode, rate) pairs does not depend on the number of available pairs, which is a crucial advantage as evolving 802.11 standards offer an increasingly large number of (mode, rate) pairs. We illustrate the efficiency of ORS algorithms (compared to the state-of-the-art algorithms) using simulations and traces extracted from 802.11 test-beds.

Connected through hyperedge HE-1 (induced by source paper "constrained thompson sampling for wireless link optimization"):
  with members:
    - "beyond the click through rate web link selection with multi level feedback"
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "unimodal thompson sampling for graph structured arms"
    - "dynamic rate and channel selection in cognitive radio systems"
    - "bandits with knapsacks"
    - "further optimal regret bounds for thompson sampling"
    - "coding versus arq in fading channels how reliable should the phy be"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-2 (induced by source paper "thresholding graph bandits with grapl"):
  with members:
    - "thresholding bandits with augmented ucb"

Connected through hyperedge HE-3 (induced by source paper "onets online network slice broker from theory to practice"):
  with members:
    - "from network sharing to multi tenancy the 5g network slice broker"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-4 (induced by source paper "collaborative spatial reuse in wireless networks via selfish multi armed bandits"):
  with members:
    - "implications of decentralized q learning resource allocation in wireless networks"
    - "breaking the interference barrier in dense wireless networks with interference alignment"
    - "ieee 802 11ax high efficiency wlans"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"
    - "further optimal regret bounds for thompson sampling"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-5 (induced by source paper "efficient beam alignment in millimeter wave systems using contextual bandits"):
  with members:
    - "agile millimeter wave networks with provable guarantees"
    - "millimeter wave beam selection using out of band spatial information"
    - "energy efficient power and bandwidth allocation in an integrated sub 6 ghz millimeter wave system"
    - "coordinated initial access in millimetre wave standalone networks"
    - "unimodal thompson sampling for graph structured arms"
    - "unimodal bandits regret lower bounds and optimal algorithms"
    - "directional cell discovery in millimeter wave cellular networks"
    - "dynamic rate and channel selection in cognitive radio systems"

Connected through hyperedge HE-6 (induced by source paper "experimental evaluation of large scale wifi multicast rate control"):
  with members:
    - "constant delay and constant feedback moving window network coding for wireless multicast design and asymptotic analysis"

Connected through hyperedge HE-7 (induced by source paper "algorithms with logarithmic or sublinear regret for constrained contextual bandits"):
  with members:
    - "thompson sampling for budgeted multi armed bandits"
    - "contextual bandits with global constraints and objective"
    - "a survey on contextual multi armed bandits"
    - "linear contextual bandits with global constraints and objective"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "bandits with knapsacks"

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)