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 clustering of linear bandits in peer to peer networks"
  Abstract: We provide two distributed confidence ball algorithms for solving linear bandit problems in peer to peer networks with limited communication capabilities. For the first, we assume that all the peers are solving the same linear bandit problem, and prove that our algorithm achieves the optimal asymptotic regret rate of any centralised algorithm that can instantly communicate information between the peers. For the second, we assume that there are clusters of peers solving the same bandit problem within each cluster, and we prove that our algorithm discovers these clusters, while achieving the optimal asymptotic regret rate within each one. Through experiments on several real-world datasets, we demonstrate the performance of proposed algorithms compared to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "on applications of bootstrap in continuous space reinforcement learning"
    - "on optimality of adaptive linear quadratic regulators"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "least squares temporal difference learning for the linear quadratic regulator"

Connected through hyperedge HE-2 (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-3 (induced by source paper "laplacian regularized graph bandits algorithms and theoretical analysis"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "alternating linear bandits for online matrix factorization recommendation"
    - "almost optimal algorithms for linear stochastic bandits with heavy tailed payoffs"
    - "graph based recommendation system"
    - "horde of bandits using gaussian markov random fields"
    - "collaborative filtering bandits"
    - "learning laplacian matrix in smooth graph signal representations"
    - "online clustering of bandits"

Connected through hyperedge HE-4 (induced by source paper "hierarchical stochastic graphlet embedding for graph based pattern recognition"):
  with members:
    - "stochastic graphlet embedding"
    - "improved algorithm on online clustering of bandits"
    - "neural message passing for quantum chemistry"
    - "high order stochastic graphlet embedding for graph based pattern recognition"
    - "product graph based higher order contextual similarities for inexact subgraph matching"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-5 (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"
    - "decentralized cooperative stochastic multi armed bandits"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "decentralized cooperative stochastic bandits"):
  with members:
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "delay and cooperation in nonstochastic bandits"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "distributed exploration in multi armed 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-7 (induced by source paper "improved algorithm on online clustering of bandits"):
  with members:
    - "online clustering of contextual cascading bandits"
    - "bernoulli rank 1 bandits for click feedback"
    - "stochastic low rank bandits"
    - "stochastic rank 1 bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (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"
    - "multi player bandits a musical chairs approach"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "distributed exploration in multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"

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)