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: "dissecting adam the sign magnitude and variance of stochastic gradients"
  Abstract: The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of stochastic gradients, whereas the update magnitude is determined by an estimate of their relative variance. We disentangle these two aspects and analyze them in isolation, gaining insight into the mechanisms underlying ADAM. This analysis also extends recent results on adverse effects of ADAM on generalization, isolating the sign aspect as the problematic one. Transferring the variance adaptation to SGD gives rise to a novel method, completing the practitioner's toolbox for problems where ADAM fails.

Connected through hyperedge HE-1 (induced by source paper "laprop a better way to combine momentum with adaptive gradient"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "optimization for deep learning theory and algorithms"
    - "on the variance of the adaptive learning rate and beyond"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "on empirical comparisons of optimizers for deep learning"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "on the variance of the adaptive learning rate and beyond"
    - "namsg an efficient method for training neural networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-3 (induced by source paper "blockwise adaptivity faster training and better generalization in deep learning"):
  with members:
    - "a sufficient condition for convergences of adam and rmsprop"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "on the convergence of weighted adagrad with momentum for training deep neural networks"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "adashift decorrelation and convergence of adaptive learning rate methods"
    - "weighted adagrad with unified momentum"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "zo adamm zeroth order adaptive momentum method for black box optimization"):
  with members:
    - "on the convergence proof of amsgrad and a new version"
    - "on the convergence of adam and beyond"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-5 (induced by source paper "error feedback fixes signsgd and other gradient compression schemes"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "a study on overfitting in deep reinforcement learning"
    - "sparsified sgd with memory"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"

Connected through hyperedge HE-6 (induced by source paper "backpack packing more into backprop"):
  with members:
    - "a modular approach to block diagonal hessian approximations for second order optimization methods"
    - "deepobs a deep learning optimizer benchmark suite"
    - "limitations of the empirical fisher approximation"
    - "don t unroll adjoint differentiating ssa form programs"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "not all samples are created equal deep learning with importance sampling"
    - "coupling adaptive batch sizes with learning rates"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "scaling laws for the principled design initialization and preconditioning of relu networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "signsgd compressed optimisation for non convex problems"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "diagonal rescaling for neural networks"
    - "neural architecture search with reinforcement learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "limitations of the empirical fisher approximation for natural gradient descent"):
  with members:
    - "information matrices and generalization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "slang fast structured covariance approximations for bayesian deep learning with natural gradient"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "three factors influencing minima in sgd"
    - "signsgd compressed optimisation for non convex problems"
    - "noisy natural gradient as variational inference"
    - "understanding deep learning requires rethinking generalization"

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)