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: "random walk initialization for training very deep feedforward networks"
  Abstract: Training very deep networks is an important open problem in machine learning. One of many difficulties is that the norm of the back-propagated error gradient can grow or decay exponentially. Here we show that training very deep feed-forward networks (FFNs) is not as difficult as previously thought. Unlike when back-propagation is applied to a recurrent network, application to an FFN amounts to multiplying the error gradient by a different random matrix at each layer. We show that the successive application of correctly scaled random matrices to an initial vector results in a random walk of the log of the norm of the resulting vectors, and we compute the scaling that makes this walk unbiased. The variance of the random walk grows only linearly with network depth and is inversely proportional to the size of each layer. Practically, this implies a gradient whose log-norm scales with the square root of the network depth and shows that the vanishing gradient problem can be mitigated by increasing the width of the layers. Mathematical analyses and experimental results using stochastic gradient descent to optimize tasks related to the MNIST and TIMIT datasets are provided to support these claims. Equations for the optimal matrix scaling are provided for the linear and ReLU cases.

Connected through hyperedge HE-1 (induced by source paper "goldilocks neural networks"):
  with members:
    - "improving deep learning by inverse square root linear units isrlus"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "a continuum among logarithmic linear and exponential functions and its potential to improve generalization in neural networks"
    - "deep learning with s shaped rectified linear activation units"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "implementation of optical deep neural networks using the fabry perot interferometer"):
  with members:
    - "large scale optical neural networks based on photoelectric multiplication"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "kronecker recurrent units"
    - "gated orthogonal recurrent units on learning to forget"
    - "the numerics of gans"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "searching for activation functions"
    - "associative long short term memory"

Connected through hyperedge HE-3 (induced by source paper "forward stability of resnet and its variants"):
  with members:
    - "deep neural nets with interpolating function as output activation"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "mathematics of deep learning"
    - "deep residual learning and pdes on manifold"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-4 (induced by source paper "no modes left behind capturing the data distribution effectively using gans"):
  with members:
    - "multi agent diverse generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "least squares generative adversarial networks"
    - "improved generator objectives for gans"

Connected through hyperedge HE-5 (induced by source paper "learning combinations of activation functions"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "achieving human parity in conversational speech recognition"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "imagenet large scale visual recognition challenge"
    - "learning activation functions to improve deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "long short term memory and learning to learn in networks of spiking neurons"):
  with members:
    - "on the convergence of adam and beyond"
    - "meta reinforcement learning of structured exploration strategies"
    - "deep rewiring training very sparse deep networks"
    - "proximal policy optimization algorithms"
    - "learning to reinforcement learn"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "network plasticity as bayesian inference"

Connected through hyperedge HE-7 (induced by source paper "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"):
  with members:
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "training region based object detectors with online hard example mining"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "learning to refine object segments"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "the shattered gradients problem if resnets are the answer then what is the question"):
  with members:
    - "diracnets training very deep neural networks without skip connections"
    - "skip connections as effective symmetry breaking"
    - "deep online convex optimization with gated games"
    - "neural taylor approximations convergence and exploration in rectifier networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "benefits of depth in neural networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "highway and residual networks learn unrolled iterative estimation"

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)