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: "tracking the best expert in non stationary stochastic environments"
  Abstract: We study the dynamic regret of multi-armed bandit and experts problem in non-stationary stochastic environments. We introduce a new parameter $\Lambda$, which measures the total statistical variance of the loss distributions over $T$ rounds of the process, and study how this amount affects the regret. We investigate the interaction between $\Lambda$ and $\Gamma$, which counts the number of times the distributions change, as well as $\Lambda$ and $V$, which measures how far the distributions deviates over time. One striking result we find is that even when $\Gamma$, $V$, and $\Lambda$ are all restricted to constant, the regret lower bound in the bandit setting still grows with $T$. The other highlight is that in the full-information setting, a constant regret becomes achievable with constant $\Gamma$ and $\Lambda$, as it can be made independent of $T$, while with constant $V$ and $\Lambda$, the regret still has a $T^{1/3}$ dependency. We not only propose algorithms with upper bound guarantee, but prove their matching lower bounds as well.

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 "taking a hint how to leverage loss predictors in contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "improved path length regret bounds for bandits"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "fast convergence of regularized learning in games"
    - "strongly adaptive online learning"

Connected through hyperedge HE-3 (induced by source paper "adaptive and efficient algorithms for tracking the best expert"):
  with members:
    - "improved path length regret bounds for bandits"
    - "more adaptive algorithms for adversarial bandits"
    - "mirror descent meets fixed share and feels no regret"
    - "near optimal algorithms for online matrix prediction"

Connected through hyperedge HE-4 (induced by source paper "bandit convex optimization in non stationary environments"):
  with members:
    - "introduction to online convex optimization"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "adaptive online learning in dynamic environments"
    - "dynamic regret of strongly adaptive methods"
    - "corralling a band of bandit algorithms"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "online optimization competing with dynamic comparators"

Connected through hyperedge HE-5 (induced by source paper "decentralized online learning take benefits from others data without sharing your own to track global trend"):
  with members:
    - "introduction to online convex optimization"
    - "online machine learning in big data streams"
    - "proximal online gradient is optimum for dynamic regret"
    - "stochastic gradient push for distributed deep learning"
    - "adaptive online learning in dynamic environments"
    - "dynamic regret of strongly adaptive methods"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"

Connected through hyperedge HE-6 (induced by source paper "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"):
  with members:
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "dynamic regret of strongly adaptive methods"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "online optimization competing with dynamic comparators"
    - "the computational power of optimization in online learning"

Connected through hyperedge HE-7 (induced by source paper "improved path length regret bounds for bandits"):
  with members:
    - "chasing convex bodies optimally"
    - "black box reductions for parameter free online learning in banach spaces"
    - "competitively chasing convex bodies"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "adaptive online learning in dynamic environments"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"

Connected through hyperedge HE-8 (induced by source paper "equipping experts bandits with long term memory"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "improved path length regret bounds for bandits"
    - "more adaptive algorithms for adversarial bandits"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "sparsity variance and curvature in multi armed bandits"
    - "strongly adaptive online learning"
    - "the best of both worlds stochastic and adversarial 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)