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 "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"):
  Abstract: Abstract   Spatial Reuse (SR) has recently gained attention to maximize the performance of IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to be key in the development of SR solutions for next-generation WLANs, since many deployments are characterized by being uncoordinated by nature. However, the potential of decentralized mechanisms is limited by the significant lack of knowledge with respect to the overall wireless environment. To shed some light on this subject, we show the main considerations and possibilities of applying online learning to address the SR problem in uncoordinated WLANs. In particular, we provide a solution based on Multi-Armed Bandits (MABs) whereby independent WLANs dynamically adjust their frequency channel, transmit power and sensitivity threshold. To that purpose, we provide two different strategies, which refer to selfish and environment-aware learning. While the former stands for pure individual behavior, the second one considers the performance experienced by surrounding networks, thus taking into account the impact of individual actions on the environment. Through these two strategies we delve into practical issues of applying MABs in wireless networks, such as convergence guarantees or adversarial effects. Our simulation results illustrate the potential of the proposed solutions for enabling SR in future WLANs. We show that substantial improvements on network performance can be achieved regarding throughput and fairness.

Includes members:
  Member "to overlap or not to overlap enabling channel bonding in high density wlans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatial reuse in ieee 802 11ax wlans")
  Member "dynamic channel bonding in spatially distributed high density wlans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatial reuse in ieee 802 11ax wlans")
  Member "performance analysis of dynamic channel bonding in spatially distributed high density wlans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ap initiated multi user transmissions in ieee 802 11ax wlans")
  Member "implications of decentralized q learning resource allocation in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards energy efficient lpwans through learning based multi hop routing for the uplink")
  Member "ieee 802 11ax high efficiency wlans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")
  Member "multi player bandits a musical chairs approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ballooning multi armed bandits")
  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 "multi armed bandits for decentralized ap selection in enterprise wlans")

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)