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: "decentralized cooperative stochastic multi armed bandits"
  Abstract: We study a decentralized cooperative stochastic multi-armed bandit problem with $K$ arms on a network of $N$ agents. In our model, the reward distribution of each arm is agent-independent. Each agent chooses iteratively one arm to play and then communicates to her neighbors. The aim is to minimize the total network regret. We design a fully decentralized algorithm that uses a running consensus procedure to compute, with some delay, accurate estimations of the average of rewards obtained by all the agents for each arm, and then uses an upper confidence bound algorithm that accounts for the delay and error of the estimations. We analyze the algorithm and up to a constant our regret bounds are better for all networks than other algorithms designed to solve the same problem. For some graphs, our regret bounds are significantly better.

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"
    - "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"
    - "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:
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "collaborative learning of stochastic bandits over a social network"
    - "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"
    - "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"
    - "multi user lax communications a multi armed bandit approach"

Connected through hyperedge HE-4 (induced by source paper "pac guarantees for cooperative multi agent reinforcement learning with restricted communication"):
  with members:
    - "concurrent meta reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "coordinated exploration in concurrent reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "distributed clustering of linear bandits in peer to peer networks"

Connected through hyperedge HE-5 (induced by source paper "cooperative online learning keeping your neighbors updated"):
  with members:
    - "introduction to online convex optimization"
    - "delay and cooperation in nonstochastic bandits"
    - "online learning with feedback graphs beyond bandits"
    - "a generalized online mirror descent with applications to classification and regression"
    - "from bandits to experts on the value of side observations"

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)