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: "multi armed bandit learning in iot networks learning helps even in non stationary settings"
  Abstract: Setting up the future Internet of Things (IoT) networks will require to support more and more communicating devices. We prove that intelligent devices in unlicensed bands can use Multi-Armed Bandit (MAB) learning algorithms to improve resource exploitation. We evaluate the performance of two classical MAB learning algorithms, UCB1 and Thompson Sampling, to handle the decentralized decision-making of Spectrum Access, applied to IoT networks; as well as learning performance with a growing number of intelligent end-devices. We show that using learning algorithms does help to fit more devices in such networks, even when all end-devices are intelligent and are dynamically changing channel. In the studied scenario, stochastic MAB learning provides a up to 16% gain in term of successful transmission probabilities, and has near optimal performance even in non-stationary and non-i.i.d. settings with a majority of intelligent devices.

Connected through hyperedge HE-1 (induced by source paper "coordination without communication optimal regret in two players multi armed bandits"):
  with members:
    - "multi player bandits the adversarial case"
    - "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
    - "multiplayer bandits without observing collision information"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

Connected through hyperedge HE-2 (induced by source paper "observe before play multi armed bandit with pre observations"):
  with members:
    - "cost aware cascading bandits"
    - "cascading bandits for large scale recommendation problems"
    - "multi user lax communications a multi armed bandit approach"
    - "multi player bandits a musical chairs approach"
    - "combinatorial cascading bandits"
    - "cascading bandits learning to rank in the cascade model"
    - "concurrent bandits and cognitive radio networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"):
  with members:
    - "multi player bandits the adversarial case"
    - "multiplayer bandits without observing collision information"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "regret in online combinatorial optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

Connected through hyperedge HE-4 (induced by source paper "gnu radio implementation of malin multi armed bandits learning for internet of things networks"):
  with members:
    - "self organized low power iot networks a distributed learning approach"
    - "low power wide area networks an overview"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-5 (induced by source paper "a non parametric multi stage learning framework for cognitive spectrum access in iot networks"):
  with members:
    - "spectrum access in cognitive radio using a two stage reinforcement learning approach"
    - "full duplex cognitive radio a new design paradigm for enhancing spectrum usage"

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)