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: "mirror descent meets fixed share and feels no regret"
  Abstract: Mirror descent with an entropic regularizer is known to achieve shifting regret bounds that are logarithmic in the dimension. This is done using either a carefully designed projection or by a weight sharing technique. Via a novel unified analysis, we show that these two approaches deliver essentially equivalent bounds on a notion of regret generalizing shifting, adaptive, discounted, and other related regrets. Our analysis also captures and extends the generalized weight sharing technique of Bousquet and Warmuth, and can be refined in several ways, including improvements for small losses and adaptive tuning of parameters.

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"
    - "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-2 (induced by source paper "learning piecewise lipschitz functions in changing environments"):
  with members:
    - "introduction to online convex optimization"
    - "data driven clustering via parameterized lloyd s families"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "online optimization of smoothed piecewise constant functions"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"
    - "an algorithm for online k means clustering"

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"
    - "tracking the best expert in non stationary stochastic environments"
    - "near optimal algorithms for online matrix prediction"

Connected through hyperedge HE-4 (induced by source paper "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"):
  with members:
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "a convergence theory for deep learning via over parameterization"
    - "geometry of optimization and implicit regularization in deep learning"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"

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"
    - "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"

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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "adaptive hedging under delayed feedback"):
  with members:
    - "introduction to online convex optimization"
    - "aggregating strategies for long term forecasting"
    - "fast rates in statistical and online learning"
    - "second order quantile methods for experts and combinatorial games"
    - "follow the leader if you can hedge if you must"
    - "prediction by random walk perturbation"
    - "online learning under delayed feedback"
    - "an identity for kernel ridge regression"

Connected through hyperedge HE-8 (induced by source paper "online adaptive principal component analysis and its extensions"):
  with members:
    - "spectral filtering for general linear dynamical systems"
    - "global convergence of policy gradient methods for linearized control problems"
    - "online convex optimization for cumulative constraints"
    - "efficient regret minimization in non convex games"

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)