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: "stochastic gradient mirror descent minimax optimality and implicit regularization"
  Abstract: Stochastic descent methods (of the gradient and mirror varieties) have become increasingly popular in optimization. In fact, it is now widely recognized that the success of deep learning is not only due to the special deep architecture of the models, but also due to the behavior of the stochastic descent methods used, which play a key role in reaching "good" solutions that generalize well to unseen data. In an attempt to shed some light on why this is the case, we revisit some minimax properties of stochastic gradient descent (SGD) for the square loss of linear models---originally developed in the 1990's---and extend them to general stochastic mirror descent (SMD) algorithms for general loss functions and nonlinear models. In particular, we show that there is a fundamental identity which holds for SMD (and SGD) under very general conditions, and which implies the minimax optimality of SMD (and SGD) for sufficiently small step size, and for a general class of loss functions and general nonlinear models. We further show that this identity can be used to naturally establish other properties of SMD (and SGD), namely convergence and implicit regularization for over-parameterized linear models (in what is now being called the "interpolating regime"), some of which have been shown in certain cases in prior literature. We also argue how this identity can be used in the so-called "highly over-parameterized" nonlinear setting (where the number of parameters far exceeds the number of data points) to provide insights into why SMD (and SGD) may have similar convergence and implicit regularization properties for deep learning.

Connected through hyperedge HE-1 (induced by source paper "on the distance between two neural networks and the stability of learning"):
  with members:
    - "optimization for deep learning theory and algorithms"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"
    - "huggingface s transformers state of the art natural language processing"
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "measuring and regularizing networks in function space"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "overfitting can be harmless for basis pursuit only to a degree"):
  with members:
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "why deep neural networks for function approximation"

Connected through hyperedge HE-3 (induced by source paper "orthogonal gradient descent for continual learning"):
  with members:
    - "prototype reminding for continual learning"
    - "uncertainty guided continual learning with bayesian neural networks"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "towards federated learning at scale system design"
    - "variational prototype replays for continual learning"
    - "attention based structural plasticity"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"

Connected through hyperedge HE-4 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "high dimensional robust sparse regression"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "deep learning is robust to massive label noise"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "policy optimization with stochastic mirror descent"):
  with members:
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "trajectory wise control variates for variance reduction in policy gradient methods"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "predictor corrector policy optimization"
    - "stochastic variance reduced policy gradient"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-6 (induced by source paper "on dropout and nuclear norm regularization"):
  with members:
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the implicit bias of dropout"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "a comprehensive analysis of deep regression"
    - "deep linear neural networks with arbitrary loss all local minima are global"

Connected through hyperedge HE-7 (induced by source paper "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"):
  with members:
    - "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"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"

Connected through hyperedge HE-8 (induced by source paper "beyond online balanced descent an optimal algorithm for smoothed online optimization"):
  with members:
    - "introduction to online convex optimization"
    - "competitive ratio versus regret minimization achieving the best of both worlds"
    - "a nearly linear bound for chasing nested convex bodies"
    - "competitively chasing convex bodies"
    - "better bounds for online line chasing"
    - "potential function proofs for first order methods"
    - "nested convex bodies are chaseable"
    - "online convex optimization using predictions"

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)