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: "coded aperture compressive temporal imaging"
  Abstract: We use mechanical translation of a coded aperture for code division multiple access compression of video. We discuss the compressed video’s temporal resolution and present experimental results for reconstructions of > 10 frames of temporal data per coded snapshot.

Connected through hyperedge HE-1 (induced by source paper "event driven video frame synthesis"):
  with members:
    - "gated fusion network for joint image deblurring and super resolution"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "learning to extract a video sequence from a single motion blurred image"
    - "continuous time intensity estimation using event cameras"
    - "bringing a blurry frame alive at high frame rate with an event camera"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-2 (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"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "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"

Connected through hyperedge HE-3 (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"
    - "deep fully connected networks for video compressive 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"

Connected through hyperedge HE-4 (induced by source paper "nonlocal low rank tensor factor analysis for image restoration"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "block wise lensless compressive camera"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "a deep learning approach to structured signal recovery"
    - "lensless compressive imaging"

Connected through hyperedge HE-5 (induced by source paper "block wise lensless compressive camera"):
  with members:
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "lensless compressive imaging"
    - "compressive hyperspectral imaging with side information"
    - "flatcam thin bare sensor cameras using coded aperture and computation"
    - "lensless imaging by compressive sensing"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-6 (induced by source paper "optimizing matrices for compressed sensing using existing goodness measures negative results and an alternative"):
  with members:
    - "optimizing codes for source separation in compressed video recovery and color image demosaicing"
    - "communications inspired projection design with application to compressive sensing"
    - "stable restoration and separation of approximately sparse signals"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-7 (induced by source paper "temporal shape super resolution by intra frame motion encoding using high fps structured light"):
  with members:
    - "depth extraction from video using non parametric sampling"
    - "compressive acquisition of dynamic scenes"

Connected through hyperedge HE-8 (induced by source paper "deepbinarymask learning a binary mask for video compressive sensing"):
  with members:
    - "rank minimization for snapshot compressive imaging"
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "snapshot compressed sensing performance bounds and algorithms"
    - "deep fully connected networks for video compressive sensing"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "context encoders feature learning by inpainting"

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)