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: "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
  Abstract: We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first $\sqrt{T}$-type regret guarantee for this problem, under the feedback model where collisions are announced to the colliding players. Such a bound was not known even for the simpler stochastic version. We also prove the first sublinear guarantee for the feedback model where collision information is not available, namely $T^{1-\frac{1}{2m}}$ where $m$ is the number of players.

Connected through hyperedge HE-1 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits"):
  with members:
    - "multi player bandits the adversarial case"
    - "competing bandits in matching markets"
    - "multi player multi armed bandits with non zero rewards on collisions for uncoordinated spectrum access"
    - "individual regret in cooperative nonstochastic multi armed bandits"
    - "social learning in multi agent multi armed bandits"
    - "multi player bandits a trekking approach"
    - "multi player multi armed bandits for stable allocation in heterogeneous ad hoc networks"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"

Connected through hyperedge HE-2 (induced by source paper "coordination without communication optimal regret in two players multi armed bandits"):
  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 armed bandit learning in iot networks learning helps even in non stationary settings"
    - "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-3 (induced by source paper "competing bandits in matching markets"):
  with members:
    - "competing bandits the perils of exploration under competition"
    - "multiplayer bandits without observing collision information"
    - "competing bandits learning under competition"
    - "delay and cooperation in nonstochastic bandits"
    - "matching while learning"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "sic mmab synchronisation involves communication in multiplayer multi armed bandits"):
  with members:
    - "an optimal algorithm in multiplayer multi armed bandits"
    - "multiplayer bandits without observing collision information"
    - "multi user communication networks a coordinated multi armed bandit approach"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "multiple identifications in multi armed bandits"
    - "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)