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"
  Abstract: In convex stochastic optimization, convergence rates in terms of minimizing the objective have been well-established. However, in terms of making the gradients small, the best known convergence rate was $O(\varepsilon^{-8/3})$ and it was left open how to improve it. #R##N#In this paper, we improve this rate to $\tilde{O}(\varepsilon^{-2})$, which is optimal up to log factors.

Connected through hyperedge HE-1 (induced by source paper "asymmetric valleys beyond sharp and flat local minima"):
  with members:
    - "on the local minima of the empirical risk"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the loss landscape of overparameterized neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "averaging weights leads to wider optima and better generalization"
    - "risk and parameter convergence of logistic regression"
    - "revisiting small batch training for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"):
  with members:
    - "iterate averaging as regularization for stochastic gradient descent"
    - "tight analyses for non smooth stochastic gradient descent"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-3 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure"):
  with members:
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "tight analyses for non smooth stochastic gradient descent"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-4 (induced by source paper "byzantine stochastic gradient descent"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "generalized byzantine tolerant sgd"
    - "efficient algorithms for outlier robust regression"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "learning from untrusted data"
    - "robust estimators in high dimensions without the computational intractability"

Connected through hyperedge HE-5 (induced by source paper "on the convergence of adaptive gradient methods for nonconvex optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "weighted adagrad with unified momentum"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "densely connected convolutional networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "uniform convergence of gradients for non convex learning and optimization"):
  with members:
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"
    - "convergence analysis of two layer neural networks with relu activation"
    - "gradient descent learns linear dynamical systems"
    - "a vector contraction inequality for rademacher complexities"
    - "identity matters in deep learning"
    - "a geometric analysis of phase retrieval"

Connected through hyperedge HE-7 (induced by source paper "katyusha x practical momentum method for stochastic sum of nonconvex optimization"):
  with members:
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "sdca without duality regularization and individual convexity"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

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)