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: "a deep primal dual network for guided depth super resolution"
  Abstract: In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a deep primal-dual network. The joint network computes a noise-free, high-resolution estimate from a noisy, low-resolution input depth map. Additionally, a high-resolution intensity image is used to guide the reconstruction in the network. By unrolling the optimization steps of a first-order primal-dual algorithm and formulating it as a network, we can train our joint method end-to-end. This not only enables us to learn the weights of the fully convolutional network, but also to optimize all parameters of the variational method and its optimization procedure. The training of such a deep network requires a large dataset for supervision. Therefore, we generate high-quality depth maps and corresponding color images with a physically based renderer. In an exhaustive evaluation we show that our method outperforms the state-of-the-art on multiple benchmarks.

Connected through hyperedge HE-1 (induced by source paper "pyramidal edge maps based guided thermal super resolution"):
  with members:
    - "keyframe based direct thermal inertial odometry"
    - "multimodal sensor fusion in single thermal image super resolution"
    - "multimedia super resolution via deep learning a survey"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "richer convolutional features for edge detection"
    - "fully convolutional siamese networks for object tracking"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "deformable kernel networks for joint image filtering"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "learning guided convolutional network for depth completion"
    - "blade filter learning for general purpose computational photography"
    - "video frame interpolation via adaptive convolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "burst denoising with kernel prediction networks"
    - "joint image filtering with deep convolutional networks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-3 (induced by source paper "deeply supervised depth map super resolution as novel view synthesis"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "simultaneous super resolution and cross modality synthesis of 3d medical images using weakly supervised joint convolutional sparse coding"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep depth super resolution learning depth super resolution using deep convolutional neural network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "enhancenet single image super resolution through automated texture synthesis"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural network for multi modal image restoration and fusion"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "feedback network for image super resolution"
    - "deformable kernel networks for joint image filtering"
    - "fast end to end trainable guided filter"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "co occurrence filter"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"

Connected through hyperedge HE-5 (induced by source paper "inferring super resolution depth from a moving light source enhanced rgb d sensor a variational approach"):
  with members:
    - "on the well posedness of uncalibrated photometric stereo under general lighting"
    - "photometric depth super resolution"
    - "a differential volumetric approach to multi view photometric stereo"
    - "detailed surface geometry and albedo recovery from rgb d video under natural illumination"
    - "led based photometric stereo modeling calibration and numerical solution"
    - "normal integration a survey"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep depth super resolution learning depth super resolution using deep convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "guided super resolution as pixel to pixel transformation"):
  with members:
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "zero shot super resolution using deep internal learning"
    - "joint image filtering with deep convolutional networks"
    - "the fast bilateral solver"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "multimedia super resolution via deep learning a survey"):
  with members:
    - "joint convolutional neural pyramid for depth map super resolution"
    - "learning a mixture of deep networks for single image super resolution"
    - "an analysis of deep neural network models for practical applications"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "failures of gradient based deep learning"
    - "enhanced deep residual networks for single image super resolution"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "single image super resolution when model adaptation matters"

Connected through hyperedge HE-8 (induced by source paper "perceptual deep depth super resolution"):
  with members:
    - "deeply supervised depth map super resolution as novel view synthesis"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "joint convolutional neural pyramid for depth map super resolution"

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)