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: "loss functions for neural networks for image processing"
  Abstract: Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is L2. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.

Connected through hyperedge HE-1 (induced by source paper "deep learning for single image super resolution a brief review"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "super resolution based on image adapted cnn denoisers incorporating generalization of training data and internal learning in test time"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "super resolution via conditional implicit maximum likelihood estimation"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "style based variational autoencoder for real world super resolution"):
  with members:
    - "local relation networks for image recognition"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "image super resolution using very deep residual channel attention networks"
    - "residual dense network for image super resolution"
    - "mine mutual information neural estimation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "model based behavioral cloning with future image similarity learning"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "zero shot visual imitation"
    - "stochastic video generation with a learned prior"
    - "behavioral cloning from observation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "stochastic variational video prediction"
    - "dart noise injection for robust imitation learning"
    - "recurrent environment simulators"

Connected through hyperedge HE-4 (induced by source paper "tomogan low dose synchrotron x ray tomography with generative adversarial networks"):
  with members:
    - "a deep journey into super resolution a survey"
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep image prior"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-5 (induced by source paper "tomogan low dose x ray tomography with generative adversarial networks"):
  with members:
    - "a deep journey into super resolution a survey"
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep convolutional neural network for inverse problems in imaging"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "monocular depth estimation with directional consistency by deep networks"):
  with members:
    - "left right comparative recurrent model for stereo matching"
    - "unsupervised learning of depth and ego motion from video"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "crafting gbd net for object detection"
    - "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"

Connected through hyperedge HE-7 (induced by source paper "deep learning inversion of seismic data"):
  with members:
    - "video to video synthesis"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "unsupervised cross domain image generation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-8 (induced by source paper "end to end learning based ultrasound reconstruction"):
  with members:
    - "high quality ultrasonic multi line transmission through deep learning"

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)