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: "hierarchical exploration for accelerating contextual bandits"
  Abstract: Contextual bandit learning is an increasingly popular approach to optimizing recommender systems via user feedback, but can be slow to converge in practice due to the need for exploring a large feature space. In this paper, we propose a coarse-to-fine hierarchical approach for encoding prior knowledge that drastically reduces the amount of exploration required. Intuitively, user preferences can be reasonably embedded in a coarse low-dimensional feature space that can be explored efficiently, requiring exploration in the high-dimensional space only as necessary. We introduce a bandit algorithm that explores within this coarse-to-fine spectrum, and prove performance guarantees that depend on how well the coarse space captures the user's preferences. We demonstrate substantial improvement over conventional bandit algorithms through extensive simulation as well as a live user study in the setting of personalized news recommendation.

Connected through hyperedge HE-1 (induced by source paper "conversational contextual bandit algorithm and application"):
  with members:
    - "active learning in recommendation systems with multi level user preferences"
    - "collaborative filtering bandits"
    - "a gang of bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"

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"
    - "low rank bandits with latent mixtures"

Connected through hyperedge HE-3 (induced by source paper "triply robust off policy evaluation"):
  with members:
    - "doubly robust off policy evaluation with shrinkage"
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "robust regression for safe exploration in control"
    - "optimal off policy evaluation for reinforcement learning with marginalized importance sampling"
    - "batch policy learning under constraints"
    - "robust covariate shift prediction with general losses and feature views"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "toward building conversational recommender systems a contextual bandit approach"):
  with members:
    - "conversational recommender system"
    - "active learning in recommendation systems with multi level user preferences"
    - "a gang of bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-5 (induced by source paper "maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream"):
  with members:
    - "inferring parameters and structure of latent variable models by variational bayes"
    - "online learning stability and stochastic gradient descent"

Connected through hyperedge HE-6 (induced by source paper "latent contextual bandits and their application to personalized recommendations for new users"):
  with members:
    - "a survey on contextual multi armed bandits"
    - "online clustering of bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "generalized thompson sampling for contextual bandits"
    - "spectral experts for estimating mixtures of linear regressions"
    - "tensor decompositions for learning latent variable models"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "online context dependent clustering in recommendations based on exploration exploitation algorithms"):
  with members:
    - "distributed clustering of linear bandits in peer to peer networks"
    - "on context dependent clustering of bandits"
    - "online clustering of bandits"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "a gang of bandits"
    - "sample complexity of multi task reinforcement learning"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "partially observable markov decision process for recommender systems"):
  with members:
    - "continuous control with deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "playing atari with deep reinforcement learning"
    - "a contextual bandit approach to personalized news article recommendation"
    - "value function approximations for partially observable markov decision processes"

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)