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: "linearly parameterized bandits"
  Abstract: We consider bandit problems involving a large (possibly infinite) collection of arms, in which the expected reward of each arm is a linear function of an $r$-dimensional random vector $\mathbf{Z} \in \mathbb{R}^r$, where $r \geq 2$. The objective is to minimize the cumulative regret and Bayes risk. When the set of arms corresponds to the unit sphere, we prove that the regret and Bayes risk is of order $\Theta(r \sqrt{T})$, by establishing a lower bound for an arbitrary policy, and showing that a matching upper bound is obtained through a policy that alternates between exploration and exploitation phases. The phase-based policy is also shown to be effective if the set of arms satisfies a strong convexity condition. For the case of a general set of arms, we describe a near-optimal policy whose regret and Bayes risk admit upper bounds of the form $O(r \sqrt{T} \log^{3/2} T)$.

Connected through hyperedge HE-1 (induced by source paper "dynamic learning with frequent new product launches a sequential multinomial logit bandit problem"):
  with members:
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "assortment optimization under the sequential multinomial logit model"
    - "thompson sampling for the mnl bandit"

Connected through hyperedge HE-2 (induced by source paper "learning to optimize under non stationarity"):
  with members:
    - "tracking the best expert in non stationary stochastic environments"
    - "corralling a band of bandit algorithms"
    - "online optimization competing with dynamic comparators"
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "learning to optimize via posterior sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "bayesian reinforcement learning a survey"):
  with members:
    - "benchmarking for bayesian reinforcement learning"
    - "on the prior sensitivity of thompson sampling"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "a greedy approximation of bayesian reinforcement learning with probably optimistic transition model"
    - "model based bayesian exploration"
    - "further optimal regret bounds for thompson sampling"
    - "model based bayesian reinforcement learning in large structured domains"

Connected through hyperedge HE-4 (induced by source paper "sequential relevance maximization with binary feedback"):
  with members:
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "active sequential hypothesis testing"
    - "an mdp based recommender system"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "parametrized stochastic multi armed bandits with binary rewards"

Connected through hyperedge HE-5 (induced by source paper "learning to optimize via information directed sampling"):
  with members:
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "finite time analysis of kernelised contextual bandits"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret in online combinatorial optimization"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "online collaborative filtering on graphs"):
  with members:
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "efficient optimal learning for contextual bandits"
    - "faster algorithms for semi matching problems"
    - "matrix completion from noisy entries"
    - "predicting the popularity of online content"

Connected through hyperedge HE-7 (induced by source paper "learning to optimize via posterior sampling"):
  with members:
    - "generalized thompson sampling for contextual bandits"
    - "linear bandits in high dimension and recommendation systems"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "x armed bandits"

Connected through hyperedge HE-8 (induced by source paper "linear bandits in high dimension and recommendation systems"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

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)