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: "online optimization in dynamic environments improved regret rates for strongly convex problems"
  Abstract: In this paper, we address tracking of a time-varying parameter with unknown dynamics. We formalize the problem as an instance of online optimization in a dynamic setting. Using online gradient descent, we propose a method that sequentially predicts the value of the parameter and in turn suffers a loss. The objective is to minimize the accumulation of losses over the time horizon, a notion that is termed dynamic regret. While existing methods focus on convex loss functions, we consider strongly convex functions so as to provide better guarantees of performance. We derive a regret bound that captures the path-length of the time-varying parameter, defined in terms of the distance between its consecutive values. In other words, the bound represents the natural connection of tracking quality to the rate of change of the parameter. We provide numerical experiments to complement our theoretical findings.

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"
    - "dynamic regret of strongly adaptive methods"
    - "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 "distributed no regret learning in multi agent systems"):
  with members:
    - "policy regret in repeated games"
    - "negative momentum for improved game dynamics"
    - "bandit learning in concave n person games"
    - "learning in time varying games"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"
    - "delay and cooperation in nonstochastic bandits"
    - "learning and efficiency in games with dynamic population"

Connected through hyperedge HE-3 (induced by source paper "socially aware robot planning via bandit human feedback"):
  with members:
    - "socially aware motion planning with deep reinforcement learning"
    - "modeling cooperative navigation in dense human crowds"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "adaptive gradient based meta learning methods"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "online meta learning"
    - "introduction to online convex optimization"
    - "leaf a benchmark for federated settings"
    - "online gradient based mixtures for transfer modulation in meta learning"
    - "federated meta learning for recommendation"
    - "prototypical networks for few shot learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"

Connected through hyperedge HE-5 (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"
    - "dynamic regret of strongly adaptive methods"
    - "corralling a band of bandit algorithms"
    - "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-6 (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"
    - "dynamic regret of strongly adaptive methods"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"

Connected through hyperedge HE-7 (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"
    - "dynamic regret of strongly adaptive methods"
    - "fusible hsts and the randomized k server conjecture"
    - "k server via multiscale entropic regularization"

Connected through hyperedge HE-8 (induced by source paper "continuous online learning and new insights to online imitation learning"):
  with members:
    - "a reduction from reinforcement learning to no regret online learning"
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "predictor corrector policy optimization"
    - "convergence of value aggregation for imitation learning"
    - "a dynamic regret analysis and adaptive regularization algorithm for on policy robot imitation learning"
    - "accelerating imitation learning with predictive models"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"

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)