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: "multiplayer bandits without observing collision information"
  Abstract: We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider two feedback models: a model in which the players can observe whether a collision has occurred, and a more difficult setup when no collision information is available. We give the first theoretical guarantees for the second model: an algorithm with a logarithmic regret, and an algorithm with a square-root regret type that does not depend on the gaps between the means. For the first model, we give the first square-root regret bounds that do not depend on the gaps. Building on these ideas, we also give an algorithm for reaching approximate Nash equilibria quickly in stochastic anti-coordination games.

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"
    - "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"
    - "multi player bandits a musical chairs approach"

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"
    - "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
    - "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 "decentralized multi player multi armed bandits with no collision information"):
  with members:
    - "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-4 (induced by source paper "accelerated learning from recommender systems using multi armed bandit"):
  with members:
    - "session based recommendation with graph neural network"
    - "productization challenges of contextual multi armed bandits"
    - "perturbed history exploration in stochastic multi armed bandits"
    - "on the difficulty of evaluating baselines a study on recommender systems"
    - "a batched multi armed bandit approach to news headline testing"
    - "a simple deep personalized recommendation system"
    - "a survey on session based recommender systems"
    - "ucboost a boosting approach to tame complexity and optimality for stochastic bandits"

Connected through hyperedge HE-5 (induced by source paper "multiplayer bandit learning from competition to cooperation"):
  with members:
    - "multi player bandits the adversarial case"
    - "the perils of exploration under competition a computational modeling approach"
    - "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-6 (induced by source paper "competing bandits in matching markets"):
  with members:
    - "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
    - "competing bandits the perils of exploration under competition"
    - "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-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "sic mmab synchronisation involves communication in multiplayer multi armed bandits"):
  with members:
    - "an optimal algorithm in multiplayer multi armed bandits"
    - "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without"
    - "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)