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: "how to make the gradients small stochastically even faster convex and nonconvex sgd"
  Abstract: Stochastic gradient descent (SGD) gives an optimal convergence rate when minimizing convex stochastic objectives $f(x)$. However, in terms of making the gradients small, the original SGD does not give an optimal rate, even when $f(x)$ is convex. #R##N#If $f(x)$ is convex, to find a point with gradient norm $\varepsilon$, we design an algorithm SGD3 with a near-optimal rate $\tilde{O}(\varepsilon^{-2})$, improving the best known rate $O(\varepsilon^{-8/3})$ of [17]. #R##N#If $f(x)$ is nonconvex, to find its $\varepsilon$-approximate local minimum, we design an algorithm SGD5 with rate $\tilde{O}(\varepsilon^{-3.5})$, where previously SGD variants only achieve $\tilde{O}(\varepsilon^{-4})$ [6, 15, 32]. This is no slower than the best known stochastic version of Newton's method in all parameter regimes [29].

Connected through hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis"):
  with members:
    - "bandit principal component analysis"
    - "exponentially convergent stochastic k pca without variance reduction"
    - "an implicit form of krasulina s k pca update without the orthonormality constraint"
    - "on the regret minimization of nonconvex online gradient ascent for online pca"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization"):
  with members:
    - "variance reduction with sparse gradients"
    - "stochastic newton and cubic newton methods with simple local linear quadratic rates"
    - "sgd general analysis and improved rates"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "stochastic cubic regularization for fast nonconvex optimization"

Connected through hyperedge HE-3 (induced by source paper "adaptive step sizes in variance reduction via regularization"):
  with members:
    - "accelerating mini batch sarah by step size rules"
    - "almost tune free variance reduction"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "on the convergence of adam and beyond"
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "on the convergence of sarah and beyond"
    - "adam a method for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-4 (induced by source paper "the complexity of finding stationary points with stochastic gradient descent"):
  with members:
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "the complexity of making the gradient small in stochastic convex optimization"):
  with members:
    - "how to escape saddle points efficiently"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-6 (induced by source paper "federated optimization for heterogeneous networks"):
  with members:
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "loadaboost loss based adaboost federated machine learning on medical data"
    - "leaf a benchmark for federated settings"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "don t use large mini batches use local sgd"

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)