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: "normalized gradient with adaptive stepsize method for deep neural network training"
  Abstract: In this paper, we propose a generic and simple algorithmic framework for first order optimization. The framework essentially contains two consecutive steps in each iteration: 1) computing and normalizing the mini-batch stochastic gradient; 2) selecting adaptive step size to update the decision variable (parameter) towards the negative of the normalized gradient. We show that the proposed approach, when customized to the popular adaptive stepsize methods, such as AdaGrad, can enjoy a sublinear convergence rate, if the objective is convex. We also conduct extensive empirical studies on various non-convex neural network optimization problems, including multi layer perceptron, convolution neural networks and recurrent neural networks. The results indicate the normalized gradient with adaptive step size can help accelerate the training of neural networks. In particular, significant speedup can be observed if the networks are deep or the dependencies are long.

Connected through hyperedge HE-1 (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-2 (induced by source paper "layer rotation a surprisingly simple indicator of generalization in deep networks"):
  with members:
    - "stiffness a new perspective on generalization in neural networks"
    - "exploring generalization in deep learning"
    - "fisher rao metric geometry and complexity of neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "wide residual networks"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "on layer level control of dnn training and its impact on generalization"):
  with members:
    - "block normalized gradient method an empirical study for training deep neural network"
    - "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"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "projection based weight normalization for deep neural networks"):
  with members:
    - "triple generative adversarial nets"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "riemannian approach to batch normalization"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "neural machine translation with reconstruction"
    - "deep networks with stochastic depth"
    - "generalized backpropagation etude de cas orthogonality"
    - "identity mappings in deep residual networks"

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)