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: "why are deep nets reversible a simple theory with implications for training"
  Abstract: Generative models for deep learning are promising both to improve understanding of the model, and yield training methods requiring fewer labeled samples. #R##N#Recent works use generative model approaches to produce the deep net's input given the value of a hidden layer several levels above. However, there is no accompanying "proof of correctness" for the generative model, showing that the feedforward deep net is the correct inference method for recovering the hidden layer given the input. Furthermore, these models are complicated. #R##N#The current paper takes a more theoretical tack. It presents a very simple generative model for RELU deep nets, with the following characteristics: (i) The generative model is just the reverse of the feedforward net: if the forward transformation at a layer is $A$ then the reverse transformation is $A^T$. (This can be seen as an explanation of the old weight tying idea for denoising autoencoders.) (ii) Its correctness can be proven under a clean theoretical assumption: the edge weights in real-life deep nets behave like random numbers. Under this assumption ---which is experimentally tested on real-life nets like AlexNet--- it is formally proved that feed forward net is a correct inference method for recovering the hidden layer. #R##N#The generative model suggests a simple modification for training: use the generative model to produce synthetic data with labels and include it in the training set. Experiments are shown to support this theory of random-like deep nets; and that it helps the training.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving learning via deep net pruning"):
  with members:
    - "deep leakage from gradients"
    - "stronger l2 l2 compressed sensing without iterating"
    - "inverting deep generative models one layer at a time"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "the state of sparsity in deep neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "federated learning strategies for improving communication efficiency"
    - "split learning for health distributed deep learning without sharing raw patient data"

Connected through hyperedge HE-2 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications"):
  with members:
    - "phase retrieval under a generative prior"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"

Connected through hyperedge HE-3 (induced by source paper "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "allennlp a deep semantic natural language processing platform"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "spectrally normalized margin bounds for neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "entropy sgd biasing gradient descent into wide valleys"

Connected through hyperedge HE-4 (induced by source paper "analysis of invariance and robustness via invertibility of relu networks"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "i revnet deep invertible networks"
    - "the reversible residual network backpropagation without storing activations"
    - "mathematics of deep learning"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "regularizing cnns with locally constrained decorrelations"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-5 (induced by source paper "phase retrieval under a generative prior"):
  with members:
    - "sparse phase retrieval via truncated amplitude flow"
    - "a geometric analysis of phase retrieval"
    - "efficient compressive phase retrieval with constrained sensing vectors"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"

Connected through hyperedge HE-6 (induced by source paper "rate optimal denoising with deep neural networks"):
  with members:
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "global guarantees for enforcing deep generative priors by empirical risk"

Connected through hyperedge HE-7 (induced by source paper "global guarantees for enforcing deep generative priors by empirical risk"):
  with members:
    - "how generative adversarial networks and their variants work an overview"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "phase retrieval under a generative prior"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "a convergence theory for deep learning via over parameterization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

Connected through hyperedge HE-8 (induced by source paper "reversible architectures for arbitrarily deep residual neural networks"):
  with members:
    - "stable architectures for deep neural networks"
    - "learning across scales a multiscale method for convolution neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "learning visual reasoning without strong priors"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "mask r cnn"
    - "full resolution image compression with recurrent neural networks"
    - "aggregated residual transformations for deep neural 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)