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: "end to end learning for image burst deblurring"
  Abstract: We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. Our system is trained end-to-end using standard backpropagation on a set of artificially generated training examples, enabling competitive performance in multi-frame blind deconvolution, both with respect to quality and runtime.

Connected through hyperedge HE-1 (induced by source paper "lsd 2 joint denoising and deblurring of short and long exposure images with convolutional neural networks"):
  with members:
    - "noise2noise learning image restoration without clean data"
    - "gyroscope aided motion deblurring with deep networks"
    - "learning to see in the dark"
    - "deep image prior"
    - "learning blind motion deblurring"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"

Connected through hyperedge HE-2 (induced by source paper "learning blind motion deblurring"):
  with members:
    - "motion deblurring in the wild"
    - "cnn for license plate motion deblurring"
    - "a neural approach to blind motion deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "deep video deblurring"
    - "spatio temporal video autoencoder with differentiable memory"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "motion deblurring in the wild"):
  with members:
    - "cnn for license plate motion deblurring"
    - "parametric object motion from blur"
    - "a neural approach to blind motion deblurring"
    - "dynamic scene deblurring using a locally adaptive linear blur model"
    - "flownet learning optical flow with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "generalized video deblurring for dynamic scenes"
    - "learning a convolutional neural network for non uniform motion blur removal"

Connected through hyperedge HE-4 (induced by source paper "separating reflection and transmission images in the wild"):
  with members:
    - "learning blind motion deblurring"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "image to image translation with conditional adversarial networks"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "u net convolutional networks for biomedical image segmentation"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"):
  with members:
    - "stereo video deblurring"
    - "generative image modeling using style and structure adversarial networks"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "a neural approach to blind motion deblurring"
    - "accurate image super resolution using very deep convolutional networks"
    - "dense optical flow prediction from a static image"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "designing deep networks for surface normal estimation"

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)