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 fully connected networks for video compressive sensing"
  Abstract: In this work we present a deep learning framework for video compressive sensing. The proposed formulation enables recovery of video frames in a few seconds at significantly improved reconstruction quality compared to previous approaches. Our investigation starts by learning a linear mapping between video sequences and corresponding measured frames which turns out to provide promising results. We then extend the linear formulation to deep fully-connected networks and explore the performance gains using deeper architectures. Our analysis is always driven by the applicability of the proposed framework on existing compressive video architectures. Extensive simulations on several video sequences document the superiority of our approach both quantitatively and qualitatively. Finally, our analysis offers insights into understanding how dataset sizes and number of layers affect reconstruction performance while raising a few points for future investigation. #R##N#Code is available at Github: this https URL

Connected through hyperedge HE-1 (induced by source paper "adaptive image sampling using deep learning and its application on x ray fluorescence image reconstruction"):
  with members:
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "multi modal image processing based on coupled dictionary learning"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "semantic image inpainting with deep generative models"
    - "deepbinarymask learning a binary mask for video compressive sensing"
    - "on demand learning for deep image restoration"
    - "context encoders feature learning by inpainting"
    - "semantic image inpainting with perceptual and contextual losses"

Connected through hyperedge HE-2 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing"):
  with members:
    - "compression artifacts removal using convolutional neural networks"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "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-3 (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"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "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-4 (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"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "cs vqa visual question answering with compressively sensed images"):
  with members:
    - "fast integral image estimation at 1 measurement rate"
    - "hierarchical question image co attention for visual question answering"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "a deep learning approach to structured signal recovery"
    - "deep residual learning for image recognition"
    - "reconstruction free action inference from compressive imagers"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "snapshot compressed sensing performance bounds and algorithms"):
  with members:
    - "rank minimization for snapshot compressive imaging"
    - "an efficient algorithm for compression based compressed sensing"
    - "csvideonet a real time end to end learning framework for high frame rate video compressive sensing"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "compressive hyperspectral imaging with side information"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "compressive light field reconstructions using deep learning"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "learning based view synthesis for light field cameras"
    - "context encoders feature learning by inpainting"
    - "learning sensor multiplexing design through back propagation"
    - "a deep learning approach to structured signal recovery"

Connected through hyperedge HE-8 (induced by source paper "rate adaptive neural networks for spatial multiplexers"):
  with members:
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "csvideonet a real time end to end learning framework for high frame rate video compressive sensing"
    - "a deep learning approach to structured signal 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.CV(Computer Vision and Pattern Recognition)