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: "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
  Abstract: Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied to many nonconvex optimization problems in machine learning. Popular examples include training deep neural networks, dimensionality reduction, and etc. Due to the lack of convexity and the extra momentum term, the optimization theory of MSGD is still largely unknown. In this paper, we study this fundamental optimization algorithm based on the so-called "strict saddle problem." By diffusion approximation type analysis, our study shows that the momentum helps escape from saddle points, but hurts the convergence within the neighborhood of optima (if without the step size annealing). Our theoretical discovery partially corroborates the empirical success of MSGD in training deep neural networks. Moreover, our analysis applies the martingale method and "Fixed-State-Chain" method from the stochastic approximation literature, which are of independent interest.

Connected through hyperedge HE-1 (induced by source paper "rethinking the hyperparameters for fine tuning"):
  with members:
    - "task2vec task embedding for meta learning"
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "domain adaptive transfer learning with specialist models"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-2 (induced by source paper "accelerating sgd with momentum for over parameterized learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "an analysis of deep neural network models for practical applications"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "learning to defense by learning to attack"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "on evaluating adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "spectral normalization for generative adversarial networks"
    - "generating adversarial examples with adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "three factors influencing minima in sgd"
    - "stochastic hyperparameter optimization through hypernetworks"
    - "do deep nets really need weight decay and dropout"
    - "don t decay the learning rate increase the batch size"
    - "super convergence very fast training of residual networks using large learning rates"
    - "residual connections encourage iterative inference"
    - "a bayesian perspective on generalization and stochastic gradient descent"

Connected through hyperedge HE-5 (induced by source paper "towards understanding acceleration tradeoff between momentum and asynchrony in nonconvex stochastic optimization"):
  with members:
    - "deep hyperspherical learning"
    - "dropping convexity for more efficient and scalable online multiview learning"
    - "revisiting distributed synchronous sgd"
    - "online multiview representation learning dropping convexity for better efficiency"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "deep residual learning for image recognition"

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)