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: "distributed exploration in multi armed bandits"
  Abstract: We study exploration in Multi-Armed Bandits in a setting where $k$ players collaborate in order to identify an $\epsilon$-optimal arm. Our motivation comes from recent employment of bandit algorithms in computationally intensive, large-scale applications. Our results demonstrate a non-trivial tradeoff between the number of arm pulls required by each of the players, and the amount of communication between them. In particular, our main result shows that by allowing the $k$ players to communicate only once, they are able to learn $\sqrt{k}$ times faster than a single player. That is, distributing learning to $k$ players gives rise to a factor $\sqrt{k}$ parallel speed-up. We complement this result with a lower bound showing this is in general the best possible. On the other extreme, we present an algorithm that achieves the ideal factor $k$ speed-up in learning performance, with communication only logarithmic in $1/\epsilon$.

Connected through hyperedge HE-1 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits"):
  with members:
    - "competing bandits in matching markets"
    - "social learning in multi agent multi armed bandits"
    - "decentralized cooperative stochastic multi armed bandits"
    - "competing bandits learning under competition"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "collaborative learning of stochastic bandits over a social network"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-2 (induced by source paper "social learning in multi agent multi armed bandits"):
  with members:
    - "collaboratively learning the best option on graphs using bounded local memory"
    - "decentralized cooperative stochastic multi armed bandits"
    - "a distributed learning dynamics in social groups"
    - "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"
    - "human collective intelligence as distributed bayesian inference"
    - "collaborative filtering bandits"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "collaborative learning with limited interaction tight bounds for distributed exploration in multi armed bandits"):
  with members:
    - "improved algorithms for collaborative pac learning"
    - "tight bounds for collaborative pac learning via multiplicative weights"
    - "adaptive multiple arm identification"
    - "towards instance optimal bounds for best arm identification"
    - "delay and cooperation in nonstochastic bandits"
    - "multi player bandits a musical chairs approach"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "decentralized cooperative stochastic bandits"):
  with members:
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "delay and cooperation in nonstochastic bandits"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "online learning under delayed feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "distributed bandit learning near optimal regret with efficient communication"):
  with members:
    - "collaborative learning with limited interaction tight bounds for distributed exploration in multi armed bandits"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "multi player bandits a musical chairs approach"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-7 (induced by source paper "game of thrones fully distributed learning for multi player bandits"):
  with members:
    - "distributed learning for channel allocation over a shared spectrum"
    - "coordinated exploration in concurrent reinforcement learning"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "the effect of communication on noncooperative multiplayer multi armed bandit problems"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "distributed clustering of linear bandits in peer to peer networks"
    - "delay and cooperation in nonstochastic bandits"
    - "multi user lax communications a multi armed bandit approach"

Connected through hyperedge HE-8 (induced by source paper "decentralized exploration in multi armed bandits"):
  with members:
    - "corrupt bandits for preserving local privacy"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "best arm identification in linear bandits"
    - "concurrent bandits and cognitive radio networks"
    - "calibrating data to sensitivity in private data analysis"
    - "distributed non stochastic experts"
    - "composition attacks and auxiliary information in data privacy"

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)