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: "global guarantees for enforcing deep generative priors by empirical risk"
  Abstract: We examine the theoretical properties of enforcing priors provided by generative deep neural networks via empirical risk minimization. In particular we consider two models, one in which the task is to invert a generative neural network given access to its last layer and another in which the task is to invert a generative neural network given only compressive linear observations of its last layer. We establish that in both cases, in suitable regimes of network layer sizes and a randomness assumption on the network weights, that the non-convex objective function given by empirical risk minimization does not have any spurious stationary points. That is, we establish that with high probability, at any point away from small neighborhoods around two scalar multiples of the desired solution, there is a descent direction. Hence, there are no local minima, saddle points, or other stationary points outside these neighborhoods. These results constitute the first theoretical guarantees which establish the favorable global geometry of these non-convex optimization problems, and they bridge the gap between the empirical success of enforcing deep generative priors and a rigorous understanding of non-linear inverse problems.

Connected through hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "inference with deep generative priors in high dimensions"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "sunlayer stable denoising with generative networks"
    - "correction by projection denoising images with generative adversarial networks"
    - "task aware compressed sensing with generative adversarial networks"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing with invertible generative models and dependent noise"):
  with members:
    - "deep compressed sensing"
    - "invertible generative models for inverse problems mitigating representation error and dataset bias"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "invertible residual networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "density estimation using real nvp"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-3 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing"):
  with members:
    - "improved hyperspectral unmixing with endmember variability parametrized using an interpolated scaling tensor"
    - "a low rank tensor regularization strategy for hyperspectral unmixing"
    - "an unsupervised approach to solving inverse problems using generative adversarial networks"
    - "a data dependent multiscale model for hyperspectral unmixing with spectral variability"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "low rank tensor modeling for hyperspectral unmixing accounting for spectral variability"
    - "improved deep spectral convolution network for hyperspectral unmixing with multinomial mixture kernel and endmember uncertainty"
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"

Connected through hyperedge HE-4 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications"):
  with members:
    - "phase retrieval under a generative prior"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "why are deep nets reversible a simple theory with implications for training"
    - "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-5 (induced by source paper "inference with deep generative priors in high dimensions"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "inverting deep generative models one layer at a time"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "sunlayer stable denoising with generative networks"

Connected through hyperedge HE-6 (induced by source paper "image processing using multi code gan prior"):
  with members:
    - "seeing what a gan cannot generate"
    - "semantic hierarchy emerges in the deep generative representations for scene synthesis"
    - "few shot unsupervised image to image translation"
    - "on the steerability of generative adversarial networks"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "interpreting the latent space of gans for semantic face editing"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "fast and provable admm for learning with generative priors"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "algorithmic aspects of inverse problems using generative models"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "phase retrieval under a generative prior"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"

Connected through hyperedge HE-8 (induced by source paper "denoising and regularization via exploiting the structural bias of convolutional generators"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "algorithmic guarantees for inverse imaging with untrained network priors"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "rate optimal denoising with 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.IT(Information Theory)