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: "deep compressed sensing"
  Abstract: Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly reconstruction process. A recent approach that combines CS with neural network generators has removed the constraint of sparsity, but reconstruction remains slow. Here we propose a novel framework that significantly improves both the performance and speed of signal recovery by jointly training a generator and the optimisation process for reconstruction via meta-learning. We explore training the measurements with different objectives, and derive a family of models based on minimising measurement errors. We show that Generative Adversarial Nets (GANs) can be viewed as a special case in this family of models. Borrowing insights from the CS perspective, we develop a novel way of improving GANs using gradient information from the discriminator.

Connected through hyperedge HE-1 (induced by source paper "foal fast online adaptive learning for cardiac motion estimation"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "learning a probabilistic model for diffeomorphic registration"
    - "direct object recognition without line of sight using optical coherence"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "selflow self supervised learning of optical flow"
    - "learning to self train for semi supervised few shot classification"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"

Connected through hyperedge HE-2 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models"):
  with members:
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "invertible generative models for inverse problems mitigating representation error and dataset bias"
    - "fast and provable admm for learning with generative priors"
    - "blind phaseless short time fourier transform recovery"
    - "deep ptych subsampled fourier ptychography using generative priors"
    - "blind image deconvolution using deep generative priors"
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing with invertible generative models and dependent noise"):
  with members:
    - "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"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "density estimation using real nvp"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-4 (induced by source paper "logan latent optimisation for generative adversarial networks"):
  with members:
    - "adversarial video generation on complex datasets"
    - "differentiable game mechanics"
    - "cgans with projection discriminator"
    - "the mechanics of n player differentiable games"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"

Connected through hyperedge HE-5 (induced by source paper "algorithmic guarantees for inverse imaging with untrained network priors"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "one dimensional deep image prior for time series inverse problems"
    - "audio denoising with deep network priors"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep ptych subsampled fourier ptychography using generative priors"

Connected through hyperedge HE-6 (induced by source paper "optimal transport cyclegan and penalized ls for unsupervised learning in inverse problems"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "k space deep learning for accelerated mri"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-7 (induced by source paper "low shot learning with untrained neural networks for imaging inverse problems"):
  with members:
    - "algorithmic guarantees for inverse imaging with untrained network priors"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "blind image deconvolution using deep generative priors"
    - "phase retrieval under a generative prior"
    - "robust compressive phase retrieval via deep generative priors"
    - "deep image prior"

Connected through hyperedge HE-8 (induced by source paper "joint design of measurement matrix and sparse support recovery method via deep auto encoder"):
  with members:
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "deepiot compressing deep neural network structures for sensing systems with a compressor critic framework"
    - "deep learning for massive mimo csi feedback"
    - "adam a method for stochastic optimization"
    - "subspace methods for joint sparse recovery"

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)