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 "multi armed bandits for decentralized ap selection in enterprise wlans"):
  Abstract: WiFi densification leads to the existence of multiple overlapping coverage areas, which allows user stations (STAs) to choose between different Access Points (APs). The standard WiFi association method makes the STAs select the AP with the strongest signal, which in many cases leads to underutilization of some APs while overcrowding others. To mitigate this situation, Reinforcement Learning techniques such as Multi-Armed Bandits can be used to dynamically learn the optimal mapping between APs and STAs, and so redistribute the STAs among the available APs accordingly. This is an especially challenging problem since the network response observed by a given STA depends on the behavior of the others, and so it is very difficult to predict without a global view of the network. #R##N#In this paper, we focus on solving this problem in a decentralized way, where STAs independently explore the different APs inside their coverage range, and select the one that better satisfy their needs. To do it, we propose a novel approach called Opportunistic epsilon-greedy with Stickiness that halts the exploration when a suitable AP is found, only resuming the exploration after several unsatisfactory association rounds. With this approach, we reduce significantly the network response dynamics, improving the ability of the STAs to find a solution faster, as well as achieving a more efficient use of the network resources. #R##N#We investigate how the characteristics of the scenario (position of the APs and STAs, traffic loads, and channel allocation strategies) impact the learning process and the achievable performance. We also show that not all the STAs have to implement the proposed solution to improve their performance. Finally, we study the case where stations arrive progressively to the system, showing that the considered approach is also suitable in such a non-stationary set-up.

Includes members:
  Member "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatial reuse in ieee 802 11ax wlans")
  Member "introduction to multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "the tmb path loss model for 5 ghz indoor wifi scenarios on the empirical relationship between rssi mcs and spatial streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the performance of the spatial reuse operation in ieee 802 11ax wlans")
  Member "a tutorial on thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "collaborative spatial reuse in wireless networks via selfish multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatial reuse in ieee 802 11ax wlans")
  Member "ieee 802 11ax high efficiency wlans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")
  Member "decentralized ap selection in large scale wireless lans considering multi ap interference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "decentralized ap selection using multi armed bandits opportunistic epsilon greedy with stickiness")
  Member "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges")

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)