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: "collaborative learning of stochastic bandits over a social network"
  Abstract: We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social network. We perform a regret analysis of various policies in this collaborative learning setting. A key finding of this paper is that natural extensions of widely-studied single agent learning policies to the network setting need not perform well in terms of regret. In particular, we identify a class of non-altruistic and individually consistent policies, and argue by deriving regret lower bounds that they are liable to suffer a large regret in the networked setting. We also show that the learning performance can be substantially improved if the agents exploit the structure of the network, and develop a simple learning algorithm based on dominating sets of the network. Specifically, we first consider a star network, which is a common motif in hierarchical social networks, and show analytically that the hub agent can be used as an information sink to expedite learning and improve the overall regret. We also derive networkwide regret bounds for the algorithm applied to general networks. We conduct numerical experiments on a variety of networks to corroborate our analytical results.

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"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "distributed clustering of linear bandits in peer to peer networks"

Connected through hyperedge HE-2 (induced by source paper "a decentralized communication policy for multi agent multi armed bandit problems"):
  with members:
    - "decentralized cooperative stochastic multi armed bandits"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "on distributed cooperative decision making in multiarmed bandits"

Connected through hyperedge HE-3 (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"
    - "delay and cooperation in nonstochastic bandits"
    - "human collective intelligence as distributed bayesian inference"
    - "collaborative filtering bandits"
    - "multi user lax communications a multi armed bandit approach"

Connected through hyperedge HE-4 (induced by source paper "individual regret in cooperative nonstochastic multi armed bandits"):
  with members:
    - "multi player bandits the adversarial case"
    - "cooperative online learning keeping your neighbors updated"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "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-5 (induced by source paper "a communication efficient multi agent actor critic algorithm for distributed reinforcement learning"):
  with members:
    - "communication efficient distributed reinforcement learning"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"

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)