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: "sunlayer stable denoising with generative networks"
  Abstract: It has been experimentally established that deep neural networks can be used to produce good generative models for real world data. It has also been established that such generative models can be exploited to solve classical inverse problems like compressed sensing and super resolution. In this work we focus on the classical signal processing problem of image denoising. We propose a theoretical setting that uses spherical harmonics to identify what mathematical properties of the activation functions will allow signal denoising with local methods.

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"
    - "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"
    - "deep image prior"

Connected through hyperedge HE-2 (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"
    - "correction by projection denoising images with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "inverting deep generative models one layer at a time"):
  with members:
    - "asymptotics of map inference in deep networks"
    - "rate optimal denoising with deep neural networks"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "random mesh projectors for inverse problems"
    - "neural proximal gradient descent for compressive imaging"
    - "phase retrieval under a generative prior"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "correction by projection denoising images with generative adversarial 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)