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: "reward maximization under uncertainty leveraging side observations on networks"
  Abstract: We study the stochastic multi-armed bandit (MAB) problem in the presence of side-observations across actions that occur as a result of an underlying network structure. In our model, a bipartite graph captures the relationship between actions and a common set of unknowns such that choosing an action reveals observations for the unknowns that it is connected to. This models a common scenario in online social networks where users respond to their friends' activity, thus providing side information about each other's preferences. Our contributions are as follows: 1) We derive an asymptotic lower bound (with respect to time) as a function of the bi-partite network structure on the regret of any uniformly good policy that achieves the maximum long-term average reward. 2) We propose two policies - a randomized policy; and a policy based on the well-known upper confidence bound (UCB) policies - both of which explore each action at a rate that is a function of its network position. We show, under mild assumptions, that these policies achieve the asymptotic lower bound on the regret up to a multiplicative factor, independent of the network structure. Finally, we use numerical examples on a real-world social network and a routing example network to demonstrate the benefits obtained by our policies over other existing policies.

Connected through hyperedge HE-1 (induced by source paper "feedback graph regret bounds for thompson sampling and ucb"):
  with members:
    - "information directed sampling for stochastic bandits with graph feedback"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "small loss bounds for online learning with partial information"
    - "online learning with feedback graphs without the graphs"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"

Connected through hyperedge HE-2 (induced by source paper "waiting but not aging age of information and utility optimization under the pull model"):
  with members:
    - "combinatorial sleeping bandits with fairness constraints"
    - "the age of information in networks moments distributions and sampling"
    - "minimizing content staleness in dynamo style replicated storage systems"
    - "optimizing information freshness in wireless networks under general interference constraints"
    - "optimizing age of information in wireless networks with perfect channel state information"
    - "status updates through multicast networks"
    - "the age of information in multihop networks"
    - "the power of waiting for more than one response in minimizing the age of information"

Connected through hyperedge HE-3 (induced by source paper "ucboost a boosting approach to tame complexity and optimality for stochastic bandits"):
  with members:
    - "information directed sampling for stochastic bandits with graph feedback"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "information directed sampling for stochastic bandits with graph feedback"):
  with members:
    - "thompson sampling for stochastic bandits with graph feedback"
    - "online learning with feedback graphs without the graphs"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "from bandits to experts a tale of domination and independence"

Connected through hyperedge HE-5 (induced by source paper "a change detection based framework for piecewise stationary multi armed bandit problem"):
  with members:
    - "tracking the best expert in non stationary stochastic environments"
    - "thompson sampling in switching environments with bayesian online change point detection"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "analysis of thompson sampling for the multi armed bandit problem"

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)