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: "a deep learning approach to block based compressed sensing of images"
  Abstract: Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. Block-based CS is a lightweight CS approach that is mostly suitable for processing very high-dimensional images and videos: it operates on local patches, employs a low-complexity reconstruction operator and requires significantly less memory to store the sensing matrix. In this paper we present a deep learning approach for block-based CS, in which a fully-connected network performs both the block-based linear sensing and non-linear reconstruction stages. During the training phase, the sensing matrix and the non-linear reconstruction operator are \emph{jointly} optimized, and the proposed approach outperforms state-of-the-art both in terms of reconstruction quality and computation time. For example, at a 25% sensing rate the average PSNR advantage is 0.77dB and computation time is over 200-times faster.

Connected through hyperedge HE-1 (induced by source paper "multi level wavelet convolutional neural networks"):
  with members:
    - "parallel grid pooling for data augmentation"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-2 (induced by source paper "error resilient deep compressive sensing"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "multi scale deep compressive sensing network"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-3 (induced by source paper "deep learning for passive synthetic aperture radar"):
  with members:
    - "deep convolutional neural network for inverse problems in imaging"
    - "recurrent inference machines for solving inverse problems"
    - "learning to learn by gradient descent by gradient descent"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "a deep learning approach to structured signal recovery"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "convcsnet a convolutional compressive sensing framework based on deep learning"):
  with members:
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a deep learning approach to structured signal recovery"
    - "accurate image super resolution using very deep convolutional networks"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "from denoising to compressed sensing"
    - "fully convolutional networks for semantic segmentation"
    - "convolutional compressed sensing using deterministic sequences"

Connected through hyperedge HE-5 (induced by source paper "deep neural network based sparse measurement matrix for image compressed sensing"):
  with members:
    - "deep networks for compressed image sensing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a deep learning approach to structured signal recovery"
    - "group based sparse representation for image restoration"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-6 (induced by source paper "multi scale deep compressive sensing network"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "multi level wavelet cnn for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-7 (induced by source paper "an efficient deep convolutional laplacian pyramid architecture for cs reconstruction at low sampling ratios"):
  with members:
    - "a deep learning approach to structured signal recovery"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "group based sparse representation for image restoration"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-8 (induced by source paper "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"):
  with members:
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep fully connected networks for video compressive sensing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "atgv net accurate depth super resolution"
    - "tensorflow a system for large scale machine learning"

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.CV(Computer Vision and Pattern Recognition)