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: "on context dependent clustering of bandits"
  Abstract: We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner. CAB makes sharp departures from the state of the art by incorporating collaborative effects into inference as well as learning processes in a manner that seamlessly interleaving explore-exploit tradeoffs and collaborative steps. We prove regret bounds under various assumptions on the data, which exhibit a crisp dependence on the expected number of clusters over the users, a natural measure of the statistical difficulty of the learning task. Experiments on production and real-world datasets show that CAB offers significantly increased prediction performance against a representative pool of state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "augmenting visual question answering with semantic frame information in a multitask learning approach"):
  with members:
    - "linguistically informed self attention for semantic role labeling"
    - "visual question answering datasets algorithms and future challenges"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hierarchical question image co attention for visual question answering"
    - "visual7w grounded question answering in images"
    - "exploring models and data for image question answering"
    - "collaborative filtering bandits"
    - "vqa visual question answering"

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"
    - "low rank bandits with latent mixtures"
    - "collaborative filtering bandits"

Connected through hyperedge HE-3 (induced by source paper "cuttlefish a lightweight primitive for adaptive query processing"):
  with members:
    - "distributed clustering of linear bandits in peer to peer networks"
    - "comparative evaluation of big data systems on scientific image analytics workloads"
    - "caffe con troll shallow ideas to speed up deep learning"
    - "mllib machine learning in apache spark"
    - "show attend and tell neural image caption generation with visual attention"
    - "on the prior sensitivity of thompson sampling"
    - "rethinking the inception architecture for computer vision"
    - "online clustering of bandits"

Connected through hyperedge HE-4 (induced by source paper "learning contextual bandits in a non stationary environment"):
  with members:
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "efficient contextual bandits in non stationary worlds"
    - "misspecified linear bandits"
    - "collaborative filtering bandits"
    - "online clustering of bandits"
    - "a gang of bandits"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-5 (induced by source paper "a bias aware news recommendation system"):
  with members:
    - "collaborative filtering bandits"
    - "sentiment analysis in the news"

Connected through hyperedge HE-6 (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"
    - "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"
    - "hierarchical exploration for accelerating contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "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)