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: "linear bandits in high dimension and recommendation systems"
  Abstract: A large number of online services provide automated recommendations to help users to navigate through a large collection of items. New items (products, videos, songs, advertisements) are suggested on the basis of the user's past history and --when available-- her demographic profile. Recommendations have to satisfy the dual goal of helping the user to explore the space of available items, while allowing the system to probe the user's preferences. #R##N#We model this trade-off using linearly parametrized multi-armed bandits, propose a policy and prove upper and lower bounds on the cumulative "reward" that coincide up to constants in the data poor (high-dimensional) regime. Prior work on linear bandits has focused on the data rich (low-dimensional) regime and used cumulative "risk" as the figure of merit. For this data rich regime, we provide a simple modification for our policy that achieves near-optimal risk performance under more restrictive assumptions on the geometry of the problem. We test (a variation of) the scheme used for establishing achievability on the Netflix and MovieLens datasets and obtain good agreement with the qualitative predictions of the theory we develop.

Connected through hyperedge HE-1 (induced by source paper "contextual bandits with random projection"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"
    - "bandit problems with side observations"

Connected through hyperedge HE-2 (induced by source paper "satisficing in time sensitive bandit learning"):
  with members:
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "information directed sampling for stochastic bandits with graph feedback"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "a tutorial on thompson sampling"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "composable core sets for determinant maximization problems via spectral spanners"):
  with members:
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "real stable polynomials and matroids optimization and counting"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized rounding for the largest simplex problem"
    - "learning determinantal point processes"
    - "exponential inapproximability of selecting a maximum volume sub matrix"

Connected through hyperedge HE-4 (induced by source paper "online learning and decision making under generalized linear model with high dimensional data"):
  with members:
    - "a practical method for solving contextual bandit problems using decision trees"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-5 (induced by source paper "alternating linear bandits for online matrix factorization recommendation"):
  with members:
    - "online clustering of bandits"

Connected through hyperedge HE-6 (induced by source paper "sequential matrix completion"):
  with members:
    - "learning to optimize via information directed sampling"
    - "learning to optimize via posterior sampling"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "further optimal regret bounds for thompson sampling"
    - "adadelta an adaptive learning rate method"
    - "a contextual bandit approach to personalized news article recommendation"
    - "low rank matrix completion with noisy observations a quantitative comparison"
    - "matrix completion from a few entries"

Connected through hyperedge HE-7 (induced by source paper "the sample complexity of online one class collaborative filtering"):
  with members:
    - "scalable and interpretable product recommendations via overlapping co clustering"
    - "regret guarantees for item item collaborative filtering"
    - "cascading bandits learning to rank in the cascade model"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-8 (induced by source paper "time sensitive bandit learning and satisficing thompson sampling"):
  with members:
    - "x armed bandits"

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)