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 player bandits the adversarial case"
  Abstract: We consider a setting where multiple players sequentially choose among a common set of actions (arms). Motivated by a cognitive radio networks application, we assume that players incur a loss upon colliding, and that communication between players is not possible. Existing approaches assume that the system is stationary. Yet this assumption is often violated in practice, e.g., due to signal strength fluctuations. In this work, we design the first Multi-player Bandit algorithm that provably works in arbitrarily changing environments, where the losses of the arms may even be chosen by an adversary. This resolves an open problem posed by Rosenski, Shamir, and Szlak (2016).

Connected through hyperedge HE-1 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits"):
  with members:
    - "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"
    - "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
    - "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:
    - "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 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 "adaptive sampling for stochastic risk averse learning"):
  with members:
    - "distributionally robust optimization and generalization in kernel methods"
    - "introduction to online convex optimization"
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "fairness risk measures"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "minimizing the maximal loss how and why"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "multiplayer bandit learning from competition to cooperation"):
  with members:
    - "the perils of exploration under competition a computational modeling approach"
    - "multiplayer bandits without observing collision information"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "competing bandits learning under competition"
    - "price of competition and dueling games"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "distributed exploration in multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "individual regret in cooperative nonstochastic multi armed bandits"):
  with members:
    - "cooperative online learning keeping your neighbors updated"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "collaborative learning of stochastic bandits over a social network"
    - "delay and cooperation in nonstochastic bandits"
    - "multi player bandits a musical chairs approach"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "concurrent bandits and cognitive radio networks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"):
  with members:
    - "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"
    - "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"

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)