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: "low rank bandits with latent mixtures"
  Abstract: We study the task of maximizing rewards from recommending items (actions) to users sequentially interacting with a recommender system. Users are modeled as latent mixtures of C many representative user classes, where each class specifies a mean reward profile across actions. Both the user features (mixture distribution over classes) and the item features (mean reward vector per class) are unknown a priori. The user identity is the only contextual information available to the learner while interacting. This induces a low-rank structure on the matrix of expected rewards r a,b from recommending item a to user b. The problem reduces to the well-known linear bandit when either user-or item-side features are perfectly known. In the setting where each user, with its stochastically sampled taste profile, interacts only for a small number of sessions, we develop a bandit algorithm for the two-sided uncertainty. It combines the Robust Tensor Power Method of Anandkumar et al. (2014b) with the OFUL linear bandit algorithm of Abbasi-Yadkori et al. (2011). We provide the first rigorous regret analysis of this combination, showing that its regret after T user interactions is˜O(C √ BT), with B the number of users. An ingredient towards this result is a novel robustness property of OFUL, of independent interest.

Connected through hyperedge HE-1 (induced by source paper "learning near optimal policies with low inherent bellman error"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "on the optimality of sparse model based planning for markov decision processes"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "frequentist regret bounds for randomized least squares value iteration"
    - "non asymptotic gap dependent regret bounds for tabular mdps"
    - "worst case regret bounds for exploration via randomized value functions"
    - "comments on the du kakade wang yang lower bounds"
    - "tight regret bounds for model based reinforcement learning with greedy policies"

Connected through hyperedge HE-2 (induced by source paper "bilinear bandits with low rank structure"):
  with members:
    - "factored bandits"
    - "horde of bandits using gaussian markov random fields"
    - "bernoulli rank 1 bandits for click feedback"
    - "stochastic low rank bandits"
    - "stochastic rank 1 bandits"
    - "contextual bandits with latent confounders an nmf approach"
    - "on context dependent clustering of bandits"
    - "collaborative filtering bandits"

Connected through hyperedge HE-3 (induced by source paper "parallel contextual bandits in wireless handover optimization"):
  with members:
    - "stochastic bandit models for delayed conversions"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "online learning under delayed feedback"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "multiarmed bandit problems with delayed feedback"

Connected through hyperedge HE-4 (induced by source paper "low rank bandit methods for high dimensional dynamic pricing"):
  with members:
    - "perishability of data dynamic pricing under varying coefficient models"
    - "online learning with low rank experts"
    - "contextual bandits with latent confounders an nmf approach"
    - "kernel based methods for bandit convex optimization"
    - "on graduated optimization for stochastic non convex problems"
    - "the best of both worlds stochastic and adversarial bandits"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-5 (induced by source paper "misspecified linear bandits"):
  with members:
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning in rich observation mdps using spectral methods"):
  with members:
    - "experimental results reinforcement learning of pomdps using spectral methods"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "a pac rl algorithm for episodic pomdps"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"

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)