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: "adaptive online learning in dynamic environments"
  Abstract: In this paper, we study online convex optimization in dynamic environments, and aim to bound the dynamic regret with respect to any sequence of comparators. Existing work have shown that online gradient descent enjoys an $O(\sqrt{T}(1+P_T))$ dynamic regret, where $T$ is the number of iterations and $P_T$ is the path-length of the comparator sequence. However, this result is unsatisfactory, as there exists a large gap from the $\Omega(\sqrt{T(1+P_T)})$ lower bound established in our paper. To address this limitation, we develop a novel online method, namely adaptive learning for dynamic environment (Ader), which achieves an optimal $O(\sqrt{T(1+P_T)})$ dynamic regret. The basic idea is to maintain a set of experts, each attaining an optimal dynamic regret for a specific path-length, and combines them with an expert-tracking algorithm. Furthermore, we propose an improved Ader based on the surrogate loss, and in this way the number of gradient evaluations per round is reduced from $O(\log T)$ to $1$. Finally, we extend Ader to the setting that a sequence of dynamical models is available to characterize the comparators.

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"
    - "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"
    - "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 "the adversarial robustness of sampling"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "introduction to online convex optimization"
    - "weighted reservoir sampling from distributed streams"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic bandits robust to adversarial corruptions"
    - "the everlasting database statistical validity at a fair price"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-3 (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"
    - "tracking the best expert in non stationary stochastic 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-4 (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"
    - "tracking the best expert in non stationary stochastic 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-5 (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"
    - "dynamic regret of strongly adaptive methods"
    - "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-6 (induced by source paper "adaptive regret of convex and smooth functions"):
  with members:
    - "introduction to online convex optimization"
    - "training deep networks without learning rates through coin betting"
    - "dynamic regret of strongly adaptive methods"
    - "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 "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"
    - "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-8 (induced by source paper "an online learning approach to model predictive control"):
  with members:
    - "introduction to online convex optimization"
    - "mirror descent search and its acceleration"
    - "autorally an open platform for aggressive autonomous driving"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "vision based high speed driving with a deep dynamic observer"
    - "acceleration of gradient based path integral method for efficient optimal and inverse optimal control"
    - "risk sensitive path integral control"
    - "theoretical foundation for cma es from information geometric perspective"

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)