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: "implications of decentralized q learning resource allocation in wireless networks"
  Abstract: Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results. In this work we propose a stateless variation of Q-learning, which we apply to exploit spatial reuse in a wireless network. In particular, we allow networks to modify both their transmission power and the channel used solely based on the experienced throughput. We concentrate in a completely decentralized scenario in which no information about neighbouring nodes is available to the learners. Our results show that although the algorithm is able to find the best-performing actions to enhance aggregate throughput, there is high variability in the throughput experienced by the individual networks. We identify the cause of this variability as the adversarial setting of our setup, in which the most played actions provide intermittent good/poor performance depending on the neighbouring decisions. We also evaluate the effect of the intrinsic learning parameters of the algorithm on this variability.

Connected through hyperedge HE-1 (induced by source paper "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"):
  with members:
    - "to overlap or not to overlap enabling channel bonding in high density wlans"
    - "dynamic channel bonding in spatially distributed high density wlans"
    - "performance analysis of dynamic channel bonding in spatially distributed high density wlans"
    - "ieee 802 11ax high efficiency wlans"
    - "multi player bandits a musical chairs approach"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "towards energy efficient lpwans through learning based multi hop routing for the uplink"):
  with members:
    - "collaborative spatial reuse in wireless networks via selfish multi armed bandits"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

Connected through hyperedge HE-3 (induced by source paper "towards energy efficient lpwans through learning based multi hop routing"):
  with members:
    - "collaborative spatial reuse in wireless networks via selfish multi armed bandits"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

Connected through hyperedge HE-4 (induced by source paper "collaborative spatial reuse in wireless networks via selfish multi armed bandits"):
  with members:
    - "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"
    - "optimal rate sampling in 802 11 systems"
    - "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"

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)