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: "escaping saddle points with adaptive gradient methods"
  Abstract: Adaptive methods such as Adam and RMSProp are widely used in deep learning but are not well understood. In this paper, we seek a crisp, clean and precise characterization of their behavior in nonconvex settings. To this end, we first provide a novel view of adaptive methods as preconditioned SGD, where the preconditioner is estimated in an online manner. By studying the preconditioner on its own, we elucidate its purpose: it rescales the stochastic gradient noise to be isotropic near stationary points, which helps escape saddle points. Furthermore, we show that adaptive methods can efficiently estimate the aforementioned preconditioner. By gluing together these two components, we provide the first (to our knowledge) second-order convergence result for any adaptive method. The key insight from our analysis is that, compared to SGD, adaptive methods escape saddle points faster, and can converge faster overall to second-order stationary points.

Connected through hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"
    - "extreme tensoring for low memory preconditioning"
    - "on empirical comparisons of optimizers for deep learning"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "an exponential learning rate schedule for deep learning"
    - "deepobs a deep learning optimizer benchmark suite"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-2 (induced by source paper "hessian based analysis of sgd for deep nets dynamics and generalization"):
  with members:
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on the convergence of adam and beyond"
    - "positively scale invariant flatness of relu neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"

Connected through hyperedge HE-3 (induced by source paper "calibrating the adaptive learning rate to improve convergence of adam"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "an alternative view when does sgd escape local minima"
    - "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"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-4 (induced by source paper "on the convergence of perturbed distributed asynchronous stochastic gradient descent to second order stationary points in non convex optimization"):
  with members:
    - "taming convergence for asynchronous stochastic gradient descent with unbounded delay in non convex learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "global optimality in low rank matrix optimization"
    - "matrix completion has no spurious local minimum"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "deep learning with elastic averaging sgd"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"

Connected through hyperedge HE-5 (induced by source paper "preconditioning kaczmarz method by sketching"):
  with members:
    - "quadrature based features for kernel approximation"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "adam a method for stochastic optimization"

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)