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: "checkerboard artifact free sub pixel convolution a note on sub pixel convolution resize convolution and convolution resize"
  Abstract: The most prominent problem associated with the deconvolution layer is the presence of checkerboard artifacts in output images and dense labels. To combat this problem, smoothness constraints, post processing and different architecture designs have been proposed. Odena et al. highlight three sources of checkerboard artifacts: deconvolution overlap, random initialization and loss functions. In this note, we proposed an initialization method for sub-pixel convolution known as convolution NN resize. Compared to sub-pixel convolution initialized with schemes designed for standard convolution kernels, it is free from checkerboard artifacts immediately after initialization. Compared to resize convolution, at the same computational complexity, it has more modelling power and converges to solutions with smaller test errors.

Connected through hyperedge HE-1 (induced by source paper "fisheyedistancenet self supervised scale aware distance estimation using monocular fisheye camera for autonomous driving"):
  with members:
    - "woodscape a multi task multi camera fisheye dataset for autonomous driving"
    - "3d packing for self supervised monocular depth estimation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "rethinking imagenet pre training"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "group normalization"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"

Connected through hyperedge HE-2 (induced by source paper "adversarial augmentation for enhancing classification of mammography images"):
  with members:
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "injecting and removing malignant features in mammography with cyclegan investigation of an automated adversarial attack using neural networks"
    - "multimodal unsupervised image to image translation"
    - "pathology segmentation using distributional differences to images of healthy origin"
    - "an adversarial learning approach to medical image synthesis for lesion removal"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "progressive growing of gans for improved quality stability and variation"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"

Connected through hyperedge HE-3 (induced by source paper "neural drum machine an interactive system for real time synthesis of drum sounds"):
  with members:
    - "variation network learning high level attributes for controlled input manipulation"
    - "fast spectrogram inversion using multi head convolutional neural networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "generative timbre spaces regularizing variational auto encoders with perceptual metrics"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "adversarial audio synthesis"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-4 (induced by source paper "exfuse enhancing feature fusion for semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "squeeze and excitation networks"
    - "understanding convolution for semantic segmentation"
    - "convolutional neural pyramid for image processing"
    - "stacked deconvolutional network for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "full resolution residual networks for semantic segmentation in street scenes"

Connected through hyperedge HE-5 (induced by source paper "dula net a dual projection network for estimating room layouts from a single rgb panorama"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "omnidepth dense depth estimation for indoors spherical panoramas"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "roomnet end to end room layout estimation"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "pano2cad room layout from a single panorama image"

Connected through hyperedge HE-6 (induced by source paper "holistic decomposition convolution for effective semantic segmentation of 3d mr images"):
  with members:
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "super resolution using convolutional neural networks without any checkerboard artifacts"):
  with members:
    - "pixel deconvolutional networks"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial 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"
    - "deeply recursive convolutional network for image super resolution"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)