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: "an efficient algorithm for learning with semi bandit feedback"
  Abstract: We consider the problem of online combinatorial optimization under semi-bandit feedback. The goal of the learner is to sequentially select its actions from a combinatorial decision set so as to minimize its cumulative loss. We propose a learning algorithm for this problem based on combining the Follow-the-Perturbed-Leader (FPL) prediction method with a novel loss estimation procedure called Geometric Resampling (GR). Contrary to previous solutions, the resulting algorithm can be efficiently implemented for any decision set where efficient offline combinatorial optimization is possible at all. Assuming that the elements of the decision set can be described with d-dimensional binary vectors with at most m non-zero entries, we show that the expected regret of our algorithm after T rounds is O(m sqrt(dT log d)). As a side result, we also improve the best known regret bounds for FPL in the full information setting to O(m^(3/2) sqrt(T log d)), gaining a factor of sqrt(d/m) over previous bounds for this algorithm.

Connected through hyperedge HE-1 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "model selection for contextual bandits"
    - "learning to optimize under non stationarity"
    - "iterate averaging as regularization for stochastic gradient descent"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "make the minority great again first order regret bound for contextual bandits"

Connected through hyperedge HE-2 (induced by source paper "online learning via the differential privacy lens"):
  with members:
    - "introduction to online convex optimization"
    - "private pac learning implies finite littlestone dimension"
    - "make the minority great again first order regret bound for contextual bandits"
    - "achieving privacy in the adversarial multi armed bandit"
    - "the price of differential privacy for online learning"
    - "adaptive learning with robust generalization guarantees"
    - "fighting bandits with a new kind of smoothness"
    - "on the generalization properties of differential privacy"

Connected through hyperedge HE-3 (induced by source paper "small loss bounds for online learning with partial information"):
  with members:
    - "make the minority great again first order regret bound for contextual bandits"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "online learning with feedback graphs without the graphs"
    - "learning in games robustness of fast convergence"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "learning and efficiency in games with dynamic population"
    - "strongly adaptive online learning"

Connected through hyperedge HE-4 (induced by source paper "beyond the hazard rate more perturbation algorithms for adversarial multi armed bandits"):
  with members:
    - "online linear optimization via smoothing"
    - "prediction by random walk perturbation"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "fpl analysis for adaptive bandits"

Connected through hyperedge HE-5 (induced by source paper "adversarial influence maximization"):
  with members:
    - "online influence maximization with local observations"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "robust influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "learning influence functions from incomplete observations"
    - "online influence maximization"

Connected through hyperedge HE-6 (induced by source paper "efficient algorithms for adversarial contextual learning"):
  with members:
    - "bistro an efficient relaxation based method for contextual bandits"
    - "fast convergence of regularized learning in games"
    - "learning in auctions regret is hard envy is easy"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "optimization learning and games with predictable sequences"
    - "efficient optimal learning for contextual bandits"
    - "efficient tracking of large classes of experts"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-7 (induced by source paper "importance weighting without importance weights an efficient algorithm for combinatorial semi bandits"):
  with members:
    - "online linear optimization via smoothing"
    - "prediction by random walk perturbation"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "regret in online combinatorial optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "the on line shortest path problem under partial monitoring"
    - "fpl analysis for adaptive bandits"

Connected through hyperedge HE-8 (induced by source paper "first order regret bounds for combinatorial semi bandits"):
  with members:
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "online linear optimization via smoothing"
    - "prediction by random walk perturbation"
    - "regret in online combinatorial optimization"
    - "the on line shortest path problem under partial monitoring"
    - "fpl analysis for adaptive bandits"
    - "prediction with expert advice by following the perturbed leader for general weights"

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)