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: "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
  Abstract: We define a general framework for a large class of combinatorial multi-armed bandit (CMAB) problems, where subsets of base arms with unknown distributions form super arms. In each round, a super arm is played and the base arms contained in the super arm are played and their outcomes are observed. We further consider the extension in which more based arms could be probabilistically triggered based on the outcomes of already triggered arms. The reward of the super arm depends on the outcomes of all played arms, and it only needs to satisfy two mild assumptions, which allow a large class of nonlinear reward instances. We assume the availability of an offline (\alpha,\beta)-approximation oracle that takes the means of the outcome distributions of arms and outputs a super arm that with probability {\beta} generates an {\alpha} fraction of the optimal expected reward. The objective of an online learning algorithm for CMAB is to minimize (\alpha,\beta)-approximation regret, which is the difference between the \alpha{\beta} fraction of the expected reward when always playing the optimal super arm, and the expected reward of playing super arms according to the algorithm. We provide CUCB algorithm that achieves O(log n) distribution-dependent regret, where n is the number of rounds played, and we further provide distribution-independent bounds for a large class of reward functions. Our regret analysis is tight in that it matches the bound of UCB1 algorithm (up to a constant factor) for the classical MAB problem, and it significantly improves the regret bound in a earlier paper on combinatorial bandits with linear rewards. We apply our CMAB framework to two new applications, probabilistic maximum coverage and social influence maximization, both having nonlinear reward structures. In particular, application to social influence maximization requires our extension on probabilistically triggered arms.

Connected through hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment"):
  with members:
    - "weighted linear bandits for non stationary environments"
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "online second price auction with semi bandit feedback under the non stationary setting"
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "learning to optimize under non stationarity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

Connected through hyperedge HE-2 (induced by source paper "tight lower bounds for combinatorial multi armed bandits"):
  with members:
    - "top k combinatorial bandits with full bandit feedback"
    - "thompson sampling for combinatorial network optimization in unknown environments"
    - "thompson sampling for combinatorial semi bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "combinatorial multi armed bandit with general reward functions"
    - "combinatorial semi bandit with known covariance"
    - "combinatorial cascading bandits"

Connected through hyperedge HE-3 (induced by source paper "contextual blocking bandits"):
  with members:
    - "variational regret bounds for reinforcement learning"
    - "blocking bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "discrepancy based algorithms for non stationary rested bandits"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-4 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "stochastic online learning with probabilistic graph feedback"
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for combinatorial semi bandits"
    - "bandits with side observations bounded vs logarithmic regret"
    - "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

Connected through hyperedge HE-5 (induced by source paper "batch size independent regret bounds for the combinatorial multi armed bandit problem"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "combinatorial multi armed bandit with general reward functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "unifying count based exploration and intrinsic motivation"
    - "combinatorial semi bandit with known covariance"
    - "combinatorial cascading bandits"

Connected through hyperedge HE-6 (induced by source paper "automatic ensemble learning for online influence maximization"):
  with members:
    - "thompson sampling for stochastic bandits with graph feedback"
    - "model independent online learning for influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "online influence maximization"
    - "influence maximization with bandits"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "an information theoretic analysis of thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-7 (induced by source paper "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"):
  with members:
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "bandit online learning with unknown delays"
    - "thompson sampling algorithms for cascading bandits"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "online learning to rank in stochastic click models"
    - "combinatorial bandits revisited"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-8 (induced by source paper "factorization bandits for online influence maximization"):
  with members:
    - "online influence maximization with local observations"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "model independent online learning for influence maximization"
    - "node2vec scalable feature learning for networks"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "online influence maximization"
    - "influence maximization with 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)