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 non stochastic experts"
  Abstract: We consider the online distributed non-stochastic experts problem, where the distributed system consists of one coordinator node that is connected to $k$ sites, and the sites are required to communicate with each other via the coordinator. At each time-step $t$, one of the $k$ site nodes has to pick an expert from the set ${1, ..., n}$, and the same site receives information about payoffs of all experts for that round. The goal of the distributed system is to minimize regret at time horizon $T$, while simultaneously keeping communication to a minimum. #R##N#The two extreme solutions to this problem are: (i) Full communication: This essentially simulates the non-distributed setting to obtain the optimal $O(\sqrt{\log(n)T})$ regret bound at the cost of $T$ communication. (ii) No communication: Each site runs an independent copy : the regret is $O(\sqrt{log(n)kT})$ and the communication is 0. This paper shows the difficulty of simultaneously achieving regret asymptotically better than $\sqrt{kT}$ and communication better than $T$. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret $O(\sqrt{k^{5(1+\epsilon)/6} T})$ and communication $O(T/k^{\epsilon})$, for any value of $\epsilon \in (0, 1/5)$. We also consider a variant of the model, where the coordinator picks the expert. In this model, we show that the label-efficient forecaster of Cesa-Bianchi et al. (2005) already gives us strategy that is near optimal in regret vs communication trade-off.

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 "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"
    - "distributed exploration in multi armed bandits"
    - "calibrating data to sensitivity in private data analysis"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-4 (induced by source paper "collaborative learning of stochastic bandits over a social network"):
  with members:
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "delay and cooperation in nonstochastic bandits"
    - "stochastic bandits on a social network collaborative learning with local information sharing"
    - "regret vs communication distributed stochastic multi armed bandits and beyond"
    - "distributed multi armed bandits regret vs communication"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "online learning with feedback graphs beyond bandits"
    - "the domination number of on line social networks and random geometric graphs"

Connected through hyperedge HE-5 (induced by source paper "network of bandits"):
  with members:
    - "best arm identification in linear bandits"
    - "distributed exploration in multi armed bandits"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-6 (induced by source paper "regret vs communication distributed stochastic multi armed bandits and beyond"):
  with members:
    - "distributed exploration in multi armed bandits"
    - "online learning under delayed feedback"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "distributed exploration in multi armed bandits"):
  with members:
    - "efficient protocols for distributed classification and optimization"
    - "distributed learning communication complexity and privacy"
    - "optimal distributed online prediction using mini batches"

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)