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: "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
  Abstract: We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, \texttt{GLR-CUCB}, which incorporates an efficient combinatorial semi-bandit algorithm, \texttt{CUCB}, with an almost parameter-free change-point detector, the \emph{Generalized Likelihood Ratio Test} (GLRT). Our analysis shows that the regret of \texttt{GLR-CUCB} is upper bounded by $\mathcal{O}(\sqrt{NKT\log{T}})$, where $N$ is the number of piecewise-stationary segments, $K$ is the number of base arms, and $T$ is the number of time steps. As a complement, we also derive a nearly matching regret lower bound on the order of $\Omega(\sqrt{NKT}$), for both piecewise-stationary multi-armed bandits and combinatorial semi-bandits, using information-theoretic techniques and judiciously constructed piecewise-stationary bandit instances. Our lower bound is tighter than the best available regret lower bound, which is $\Omega(\sqrt{T})$. Numerical experiments on both synthetic and real-world datasets demonstrate the superiority of \texttt{GLR-CUCB} compared to other state-of-the-art algorithms.

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"
    - "learning to optimize under non stationarity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"

Connected through hyperedge HE-2 (induced by source paper "nearly optimal algorithms for piecewise stationary cascading bandits"):
  with members:
    - "combinatorial semi bandit in the non stationary environment"
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "more adaptive algorithms for adversarial bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "collaborative filtering bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-3 (induced by source paper "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"):
  with members:
    - "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"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

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)