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: "reconnet non iterative reconstruction of images from compressively sensed random measurements"
  Abstract: The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image. On a standard dataset of images we show significant improvements in reconstruction results (both in terms of PSNR and time complexity) over state-of-the-art iterative CS reconstruction algorithms at various measurement rates. Further, through qualitative experiments on real data collected using our block single pixel camera (SPC), we show that our network is highly robust to sensor noise and can recover visually better quality images than competitive algorithms at extremely low sensing rates of 0.1 and 0.04. To demonstrate that our algorithm can recover semantically informative images even at a low measurement rate of 0.01, we present a very robust proof of concept real-time visual tracking application.

Connected through hyperedge HE-1 (induced by source paper "ada lista learned solvers adaptive to varying models"):
  with members:
    - "deep network classification by scattering and homotopy dictionary learning"
    - "deep energy using energy functions for unsupervised training of dnns"
    - "deep energy unsupervised training of deep neural networks"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "learning to learn by gradient descent by gradient descent"
    - "the little engine that could regularization by denoising red"

Connected through hyperedge HE-2 (induced by source paper "plug and play methods provably converge with properly trained denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "denoising prior driven deep neural network for image restoration"
    - "regularization by denoising clarifications and new interpretations"
    - "regularized fourier ptychography using an online plug and play algorithm"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep compressed sensing"):
  with members:
    - "cgans with projection discriminator"
    - "the gan landscape losses architectures regularization and normalization"
    - "the mechanics of n player differentiable games"
    - "spectral normalization for generative adversarial networks"
    - "convcsnet a convolutional compressive sensing framework based on deep learning"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "on unifying deep generative models"

Connected through hyperedge HE-4 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing"):
  with members:
    - "deep fully connected networks for video compressive sensing"
    - "compression artifacts removal using convolutional neural networks"
    - "deepbinarymask learning a binary mask for video compressive sensing"
    - "a deep learning approach to structured signal recovery"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "rank minimization for snapshot compressive imaging"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "group based sparse representation for image restoration"
    - "from denoising to compressed sensing"
    - "coded aperture compressive temporal imaging"

Connected through hyperedge HE-6 (induced by source paper "extra proximal gradient inspired non local network"):
  with members:
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "non local neural networks"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "non local color image denoising with convolutional neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "solving inverse problems via auto encoders"):
  with members:
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep fully connected networks for video compressive sensing"
    - "new approach to bayesian high dimensional linear regression"
    - "u net convolutional networks for biomedical image segmentation"
    - "a deep learning approach to structured signal recovery"
    - "deep learning face attributes in the wild"
    - "from compression to compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "deep one bit compressive autoencoding"):
  with members:
    - "signal recovery from 1 bit quantized noisy samples via adaptive thresholding"
    - "a comparison of hybrid beamforming and digital beamforming with low resolution adcs for multiple users and imperfect csi"
    - "deep fully connected networks for video compressive sensing"
    - "a deep learning approach to structured signal recovery"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

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)