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: "dynamic regret of strongly adaptive methods"
  Abstract: To cope with changing environments, recent developments in online learning have introduced the concepts of adaptive regret and dynamic regret independently. In this paper, we illustrate an intrinsic connection between these two concepts by showing that the dynamic regret can be expressed in terms of the adaptive regret and the functional variation. This observation implies that strongly adaptive algorithms can be directly leveraged to minimize the dynamic regret. As a result, we present a series of strongly adaptive algorithms that have small dynamic regrets for convex functions, exponentially concave functions, and strongly convex functions, respectively. To the best of our knowledge, this is the first time that exponential concavity is utilized to upper bound the dynamic regret. Moreover, all of those adaptive algorithms do not need any prior knowledge of the functional variation, which is a significant advantage over previous specialized methods for minimizing dynamic regret.

Connected through hyperedge HE-1 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously"):
  with members:
    - "introduction to online convex optimization"
    - "adaptive regret of convex and smooth functions"
    - "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"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"
    - "mirror descent meets fixed share and feels no regret"

Connected through hyperedge HE-2 (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"
    - "tracking the best expert in non stationary stochastic environments"
    - "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-3 (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"
    - "tracking the best expert in non stationary stochastic 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-4 (induced by source paper "understand dynamic regret with switching cost for online decision making"):
  with members:
    - "introduction to online convex optimization"
    - "proximal online gradient is optimum for dynamic regret"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "metrical task systems on trees via mirror descent and unfair gluing"
    - "adaptive online learning in dynamic environments"
    - "fusible hsts and the randomized k server conjecture"
    - "k server via multiscale entropic regularization"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"

Connected through hyperedge HE-5 (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"
    - "tracking the best expert in non stationary stochastic environments"
    - "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-6 (induced by source paper "adaptive regret of convex and smooth functions"):
  with members:
    - "introduction to online convex optimization"
    - "adaptive online learning in dynamic environments"
    - "training deep networks without learning rates through coin betting"
    - "scale free online learning"
    - "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"
    - "strongly adaptive online learning"

Connected through hyperedge HE-7 (induced by source paper "dynamic local regret for non convex online forecasting"):
  with members:
    - "spectral filtering for general linear dynamical systems"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "efficient regret minimization in non convex games"
    - "online forecasting matrix factorization"
    - "strongly adaptive online learning"
    - "online learning for time series prediction"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-8 (induced by source paper "dual adaptivity a universal algorithm for minimizing the adaptive regret of convex functions"):
  with members:
    - "adaptivity and optimality a universal algorithm for online convex optimization"
    - "introduction to online convex optimization"
    - "adaptive regret of convex and smooth functions"
    - "metagrad multiple learning rates in online learning"
    - "strongly adaptive online learning"
    - "efficient tracking of large classes of experts"

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)