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: "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
  Abstract: Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage. We present a decentralized algorithm that achieves the same performance as a centralized one, contradicting the existing lower bounds for that problem. This is possible by "hacking" the standard model by constructing a communication protocol between players that deliberately enforces collisions, allowing them to share their information at a negligible cost. This motivates the introduction of a more appropriate dynamic setting without sensing, where similar communication protocols are no longer possible. However, we show that the logarithmic growth of the regret is still achievable for this model with a new algorithm.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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-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"
    - "multiplayer bandits without observing collision information"

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"
    - "multiplayer bandits without observing collision information"
    - "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 "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"
    - "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-7 (induced by source paper "multi user mabs with user dependent rewards for uncoordinated spectrum access"):
  with members:
    - "multi player multi armed bandits with non zero rewards on collisions for uncoordinated spectrum access"
    - "multi user multi armed bandits for uncoordinated spectrum access"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"

Connected through hyperedge HE-8 (induced by source paper "upper confidence bound for channel selection in lpwa networks with retransmissions"):
  with members:
    - "self organized low power iot networks a distributed learning approach"
    - "low power wide area networks an overview"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)