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: "linear convergence of gradient and proximal gradient methods under the polyak l ojasiewicz condition"
  Abstract: In 1963, Polyak proposed a simple condition that is sufficient to show a global linear convergence rate for gradient descent. This condition is a special case of the \L{}ojasiewicz inequality proposed in the same year, and it does not require strong convexity (or even convexity). In this work, we show that this much-older Polyak-\L{}ojasiewicz (PL) inequality is actually weaker than the main conditions that have been explored to show linear convergence rates without strong convexity over the last 25 years. We also use the PL inequality to give new analyses of randomized and greedy coordinate descent methods, sign-based gradient descent methods, and stochastic gradient methods in the classic setting (with decreasing or constant step-sizes) as well as the variance-reduced setting. We further propose a generalization that applies to proximal-gradient methods for non-smooth optimization, leading to simple proofs of linear convergence of these methods. Along the way, we give simple convergence results for a wide variety of problems in machine learning: least squares, logistic regression, boosting, resilient backpropagation, L1-regularization, support vector machines, stochastic dual coordinate ascent, and stochastic variance-reduced gradient methods.

Connected through hyperedge HE-1 (induced by source paper "the error feedback framework better rates for sgd with delayed gradients and compressed communication"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "sgd general analysis and improved rates"
    - "unified optimal analysis of the stochastic gradient method"
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"

Connected through hyperedge HE-2 (induced by source paper "game theoretic optimization via gradient based nikaido isoda function"):
  with members:
    - "a variational inequality perspective on generative adversarial nets"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "learning with minimal information in continuous games"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "improved training of wasserstein gans"
    - "stabilizing adversarial nets with prediction methods"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-3 (induced by source paper "provable guarantees for gradient based meta learning"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "introduction to online convex optimization"
    - "online gradient based mixtures for transfer modulation in meta learning"
    - "auto meta automated gradient based meta learner search"
    - "fast rates for online gradient descent without strong convexity via hoffman s bound"
    - "prototypical networks for few shot learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"

Connected through hyperedge HE-4 (induced by source paper "surrogate losses for online learning of stepsizes in stochastic non convex optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "online learning rate adaptation with hypergradient descent"
    - "metagrad multiple learning rates in online learning"
    - "adam a method for stochastic optimization"
    - "a survey of algorithms and analysis for adaptive online learning"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "projected semi stochastic gradient descent method with mini batch scheme under weak strong convexity assumption"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "communication efficient distributed dual coordinate ascent"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "linear convergence of variance reduced stochastic gradient without strong convexity"
    - "mini batch primal and dual methods for svms"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

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)