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: "is l2 a good loss function for neural networks for image processing"
  Abstract: Neural networks are becoming central in several areas of computer vision and image processing. Different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention by the research community: the default and most common choice is L2. This can be particularly limiting in the context of image processing, since L2 correlates poorly with perceived image quality. #R##N#In this paper we bring attention to alternative choices. We study the performance of several losses, including perceptually-motivated losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even for the same network architecture.

Connected through hyperedge HE-1 (induced by source paper "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "self supervised learning for stereo matching with self improving ability"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised monocular stereo matching"):
  with members:
    - "single view stereo matching"
    - "unsupervised learning of depth and ego motion from video"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "semi supervised deep learning for monocular depth map prediction"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"):
  with members:
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "demon depth and motion network for learning monocular stereo"
    - "sfm net learning of structure and motion from video"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-4 (induced by source paper "gp gan towards realistic high resolution image blending"):
  with members:
    - "deep image harmonization"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "pixel level domain transfer"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "generative image modeling using style and structure adversarial networks"
    - "improved techniques for training gans"

Connected through hyperedge HE-5 (induced by source paper "unsupervised monocular depth estimation with left right consistency"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "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"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deeper depth prediction with fully convolutional residual networks"
    - "estimating depth from monocular images as classification using deep fully convolutional residual networks"

Connected through hyperedge HE-6 (induced by source paper "learning to generate images with perceptual similarity metrics"):
  with members:
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "generative moment matching networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "loss functions for neural networks for image processing"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"

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)