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: "a mean field theory of batch normalization"
  Abstract: We develop a mean field theory for batch normalization in fully-connected feedforward neural networks. In so doing, we provide a precise characterization of signal propagation and gradient backpropagation in wide batch-normalized networks at initialization. Our theory shows that gradient signals grow exponentially in depth and that these exploding gradients cannot be eliminated by tuning the initial weight variances or by adjusting the nonlinear activation function. Indeed, batch normalization itself is the cause of gradient explosion. As a result, vanilla batch-normalized networks without skip connections are not trainable at large depths for common initialization schemes, a prediction that we verify with a variety of empirical simulations. While gradient explosion cannot be eliminated, it can be reduced by tuning the network close to the linear regime, which improves the trainability of deep batch-normalized networks without residual connections. Finally, we investigate the learning dynamics of batch-normalized networks and observe that after a single step of optimization the networks achieve a relatively stable equilibrium in which gradients have dramatically smaller dynamic range. Our theory leverages Laplace, Fourier, and Gegenbauer transforms and we derive new identities that may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "batch normalization biases deep residual networks towards shallow paths"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "fixup initialization residual learning without normalization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "self training with noisy student improves imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "weight standardization"
    - "a quantization friendly separable convolution for mobilenets"
    - "group normalization"
    - "spectrum concentration in deep residual learning a free probability approach"
    - "stable architectures for deep neural networks"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-3 (induced by source paper "layer wise conditioning analysis in exploring the learning dynamics of dnns"):
  with members:
    - "mean field analysis of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "group normalization"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "large scale gan training for high fidelity natural image synthesis"
    - "proximal backpropagation"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-4 (induced by source paper "training batchnorm and only batchnorm on the expressive power of random features in cnns"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "what s hidden in a randomly weighted neural network"
    - "on the power and limitations of random features for understanding neural networks"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the shattered gradients problem if resnets are the answer then what is the question"

Connected through hyperedge HE-5 (induced by source paper "separating the effects of batch normalization on cnn training speed and stability using classical adaptive filter theory"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "pytorch an imperative style high performance deep learning library"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "all you need is a good init"
    - "fixed point quantization of deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "where is the information in a deep neural network"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "fixup initialization residual learning without normalization"
    - "stiffness a new perspective on generalization in neural networks"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"

Connected through hyperedge HE-8 (induced by source paper "towards efficient training for neural network quantization"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "differentiable quantization of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "fixup initialization residual learning without normalization"
    - "learned step size quantization"
    - "binary ensemble neural network more bits per network or more networks per bit"

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.NE(Neural and Evolutionary Computing)