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: "dr2 net deep residual reconstruction network for image compressive sensing"
  Abstract: Abstract   Most traditional algorithms for compressive sensing image reconstruction suffer from the intensive computation. Recently, deep learning-based reconstruction algorithms have been reported, which dramatically reduce the time complexity than iterative reconstruction algorithms. In this paper, we propose a novel Deep Residual Reconstruction Network (DR2-Net) to reconstruct the image from its Compressively Sensed (CS) measurement. The DR2-Net is proposed based on two observations: (1) linear mapping could reconstruct a high-quality preliminary image, and (2) residual learning could further improve the reconstruction quality. Accordingly, DR2-Net consists of two components, i.e., linear mapping network and residual network, respectively. Specifically, the fully-connected layer in neural network implements the linear mapping network. We then expand the linear mapping network to DR2-Net by adding several residual learning blocks to enhance the preliminary image. Extensive experiments demonstrate that the DR2-Net outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.04, 0.1, and 0.25, respectively. The code of DR2-Net has been released on:  https://github.com/coldrainyht/caffe_dr2 .

Connected through hyperedge HE-1 (induced by source paper "improving robustness of deep learning based image reconstruction"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "gan based projector for faster recovery with convergence guarantees in linear inverse problems"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "scaling provable adversarial defenses"
    - "adversarially robust generalization requires more data"
    - "generating adversarial examples with adversarial networks"
    - "image reconstruction by domain transform manifold learning"

Connected through hyperedge HE-2 (induced by source paper "mc ista net adaptive measurement and initialization and channel attention optimization inspired neural network for compressive sensing"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "ensemble one dimensional convolution neural networks for skeleton based action recognition"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "denoising based vector approximate message passing"
    - "deep residual learning for image recognition"
    - "new constructions of rip matrices with fast multiplication and fewer rows"

Connected through hyperedge HE-3 (induced by source paper "multi resolution csi feedback with deep learning in massive mimo system"):
  with members:
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "deep learning for massive mimo csi feedback"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "compressed channel feedback for correlated massive mimo systems"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-4 (induced by source paper "error resilient deep compressive sensing"):
  with members:
    - "multi scale deep compressive sensing network"
    - "a deep learning approach to block based compressed sensing of images"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-5 (induced by source paper "convolutional neural network for fourier ptychography video reconstruction learning temporal dynamics from spatial ensembles"):
  with members:
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "learning from weak and noisy labels for semantic segmentation"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "solving inverse computational imaging problems using deep pixel level prior"):
  with members:
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "lensless computational imaging through deep learning"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "learning deep cnn denoiser prior for image restoration"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "compressive image recovery using recurrent generative model"

Connected through hyperedge HE-7 (induced by source paper "multi scale deep compressive sensing network"):
  with members:
    - "multi level wavelet cnn for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a deep learning approach to block based compressed sensing of images"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-8 (induced by source paper "deep learning for massive mimo csi feedback"):
  with members:
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep learning based mimo communications"
    - "convolutional neural networks for non iterative reconstruction of compressively sensed images"
    - "deep residual learning for image recognition"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "from denoising to compressed sensing"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "on capacity of large scale mimo multiple access channels with distributed sets of correlated antennas"

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)