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 with general reward functions"
  Abstract: In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework that allows a general nonlinear reward function, whose expected value may not depend only on the means of the input random variables but possibly on the entire distributions of these variables. Our framework enables a much larger class of reward functions such as the $\max()$ function and nonlinear utility functions. Existing techniques relying on accurate estimations of the means of random variables, such as the upper confidence bound (UCB) technique, do not work directly on these functions. We propose a new algorithm called stochastically dominant confidence bound (SDCB), which estimates the distributions of underlying random variables and their stochastically dominant confidence bounds. We prove that SDCB can achieve $O(\log{T})$ distribution-dependent regret and $\tilde{O}(\sqrt{T})$ distribution-independent regret, where $T$ is the time horizon. We apply our results to the $K$-MAX problem and expected utility maximization problems. In particular, for $K$-MAX, we provide the first polynomial-time approximation scheme (PTAS) for its offline problem, and give the first $\tilde{O}(\sqrt T)$ bound on the $(1-\epsilon)$-approximation regret of its online problem, for any $\epsilon>0$.

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 "distributed task replication for vehicular edge computing performance analysis and learning based algorithm"):
  with members:
    - "adaptive learning based task offloading for vehicular edge computing systems"
    - "exploiting moving intelligence delay optimized computation offloading in vehicular fog networks"
    - "heterogeneous coded computation across heterogeneous workers"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "dense moving fog for intelligent iot key challenges and opportunities"
    - "learning based task offloading for vehicular cloud computing systems"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-3 (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 semi bandit with known covariance"
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-4 (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"
    - "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"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-5 (induced by source paper "thompson sampling for a fatigue aware online recommendation system"):
  with members:
    - "dynamic learning of sequential choice bandit problem under marketing fatigue"
    - "thompson sampling for combinatorial semi bandits"
    - "thompson sampling for cascading bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "a tutorial on thompson sampling"
    - "thompson sampling for the mnl bandit"
    - "cascading bandits learning to rank in the cascade model"

Connected through hyperedge HE-6 (induced by source paper "online learning for measuring incentive compatibility in ad auctions"):
  with members:
    - "learning to bid without knowing your value"
    - "multiplicative pacing equilibria in auction markets"
    - "online learning in repeated auctions"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "online learning and profit maximization from revealed preferences"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "mechanism design for data science"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "top k combinatorial bandits with full bandit feedback"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "polynomial time algorithms for combinatorial pure exploration with full bandit feedback"
    - "introduction to multi armed bandits"
    - "tight bounds for bandit combinatorial optimization"
    - "combinatorial bandits revisited"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "regret in online combinatorial optimization"

Connected through hyperedge HE-8 (induced by source paper "censored semi bandits a framework for resource allocation with censored feedback"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "a better resource allocation algorithm with semi bandit feedback"
    - "combinatorial bandits revisited"
    - "optimal resource allocation with semi bandit feedback"
    - "bandits with knapsacks"

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)