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: "block normalized gradient method an empirical study for training deep neural network"
  Abstract: In this paper, we propose a generic and simple strategy for utilizing stochastic gradient information in optimization. The technique essentially contains two consecutive steps in each iteration: 1) computing and normalizing each block (layer) of the mini-batch stochastic gradient; 2) selecting appropriate step size to update the decision variable (parameter) towards the negative of the block-normalized gradient. We conduct extensive empirical studies on various non-convex neural network optimization problems, including multilayer perceptron, convolution neural networks and recurrent neural networks. The results indicate the block-normalized gradient can help accelerate the training of neural networks. In particular, we observe that the normalized gradient methods having constant step size with occasionally decay, such as SGD with momentum, have better performance in the deep convolution neural networks, while those with adaptive step sizes, such as Adam, perform better in recurrent neural networks. Besides, we also observe this line of methods can lead to solutions with better generalization properties, which is confirmed by the performance improvement over strong baselines.

Connected through hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "nas evaluation is frustratingly hard"
    - "towards modular and programmable architecture search"
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "stochastic gradient methods with layer wise adaptive moments for training of deep networks"):
  with members:
    - "towards a human like open domain chatbot"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "transformer xl attentive language models beyond a fixed length context"
    - "on the convergence of adam and beyond"
    - "bag of tricks for image classification with convolutional neural networks"
    - "normalized direction preserving adam"
    - "scaling neural machine translation"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

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"
    - "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"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-4 (induced by source paper "the geometry of sign gradient descent"):
  with members:
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "ellipsoidal trust region methods and the marginal value of hessian information for neural network training"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "neural arabic text diacritization state of the art results and a novel approach for machine translation"):
  with members:
    - "arabic text diacritization using deep neural networks"
    - "homograph disambiguation through selective diacritic restoration"
    - "optimizing performance of recurrent neural networks on gpus"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "hybrid approaches for automatic vowelization of arabic texts"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "on layer level control of dnn training and its impact on generalization"):
  with members:
    - "normalized direction preserving adam"
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "super convergence very fast training of residual networks using large learning rates"
    - "l2 regularization versus batch and weight normalization"
    - "normalized gradient with adaptive stepsize method for deep neural network training"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"

Connected through hyperedge HE-7 (induced by source paper "normalized direction preserving adam"):
  with members:
    - "decoupled weight decay regularization"
    - "be careful what you backpropagate a case for linear output activations gradient boosting"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "wide residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "fixing a broken elbo"):
  with members:
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "emergence of invariance and disentanglement in deep representations"
    - "on unifying deep generative models"
    - "improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks"
    - "opening the black box of deep neural networks via information"
    - "vae with a vampprior"
    - "improving variational inference with inverse autoregressive flow"

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)