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 saddles with stochastic gradients"
  Abstract: We analyze the variance of stochastic gradients along negative curvature directions in certain non-convex machine learning models and show that stochastic gradients exhibit a strong component along these directions. Furthermore, we show that - contrary to the case of isotropic noise - this variance is proportional to the magnitude of the corresponding eigenvalues and not decreasing in the dimensionality. Based upon this observation we propose a new assumption under which we show that the injection of explicit, isotropic noise usually applied to make gradient descent escape saddle points can successfully be replaced by a simple SGD step. Additionally - and under the same condition - we derive the first convergence rate for plain SGD to a second-order stationary point in a number of iterations that is independent of the problem dimension.

Connected through hyperedge HE-1 (induced by source paper "improve sgd training via aligning min batches"):
  with members:
    - "convolutional networks with dense connectivity"
    - "cold case the lost mnist digits"
    - "pytorch an imperative style high performance deep learning library"
    - "dropblock a regularization method for convolutional networks"
    - "deep learning for classical japanese literature"
    - "exploring generalization in deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mmd gan towards deeper understanding of moment matching network"

Connected through hyperedge HE-2 (induced by source paper "e2 train training state of the art cnns with over 80 energy savings"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "adascale towards real time video object detection using adaptive scaling"
    - "swalp stochastic weight averaging in low precision training"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-3 (induced by source paper "over parameterization as a catalyst for better generalization of deep relu network"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "luck matters understanding training dynamics of deep relu networks"
    - "measuring the intrinsic dimension of objective landscapes"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-4 (induced by source paper "the multiplicative noise in stochastic gradient descent data dependent regularization continuous and discrete approximation"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "uniform in time weak error analysis for stochastic gradient descent algorithms via diffusion approximation"
    - "an empirical study on regularization of deep neural networks by local rademacher complexity"
    - "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "three factors influencing minima in sgd"
    - "an alternative view when does sgd escape local minima"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

Connected through hyperedge HE-5 (induced by source paper "on nonconvex optimization for machine learning gradients stochasticity and saddle points"):
  with members:
    - "finding local minima via stochastic nested variance reduction"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "a generic approach for escaping saddle points"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-6 (induced by source paper "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent"):
  with members:
    - "on the local minima of the empirical risk"
    - "an alternative view when does sgd escape local minima"
    - "stochastic modified equations and dynamics of stochastic gradient algorithms i mathematical foundations"
    - "geometry of optimization and implicit regularization in deep learning"
    - "opening the black box of deep neural networks via information"
    - "how to escape saddle points efficiently"
    - "understanding deep learning requires rethinking generalization"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-7 (induced by source paper "linear speedup in saddle point escape for decentralized non convex optimization"):
  with members:
    - "distributed learning in non convex environments part ii polynomial escape from saddle points"
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "next in network nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "communication efficient distributed dual coordinate ascent"
    - "on the learning behavior of adaptive networks part ii performance analysis"

Connected through hyperedge HE-8 (induced by source paper "distributed learning in non convex environments part ii polynomial escape from saddle points"):
  with members:
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "how to escape saddle points efficiently"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "next in network nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"

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)