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: "learning to coordinate without communication in multi user multi armed bandit problems"
  Abstract: We consider a setting where multiple users share multiple channels modeled as a multi-user multi-armed bandit (MAB) problem. The characteristics of each channel are initially unknown and may differ between the users. Each user can choose between the channels, but her success depends on the particular channel as well as on the selections of other users: if two users select the same channel their messages collide and none of them manages to send any data. Our setting is fully distributed, so there is no central control and every user only observes the channel she currently uses. As in many communication systems such as cognitive radio networks, the users cannot communicate among themselves so coordination must be achieved without direct communication. We develop algorithms for learning a stable configuration for the multiple user MAB problem. We further offer both convergence guarantees and experiments inspired by real communication networks.

Connected through hyperedge HE-1 (induced by source paper "selfish robustness and equilibria in multi player bandits"):
  with members:
    - "an optimal algorithm in multiplayer multi armed bandits"
    - "competing bandits in matching markets"
    - "introduction to multi armed bandits"
    - "multiplayer bandits without observing collision information"
    - "dynamic pricing with finitely many unknown valuations"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "multi user communication networks a coordinated multi armed bandit approach"
    - "fast rates for bandit optimization with upper confidence frank wolfe"

Connected through hyperedge HE-2 (induced by source paper "multi user multi armed bandits for uncoordinated spectrum access"):
  with members:
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "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-3 (induced by source paper "multi player bandits a musical chairs approach"):
  with members:
    - "concurrent bandits and cognitive radio networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"

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.LG(Machine Learning)