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: "fully convolutional model for variable bit length and lossy high density compression of mammograms"
  Abstract: Early works on medical image compression date to the 1980's with the impetus on deployment of teleradiology systems for high-resolution digital X-ray detectors. Commercially deployed systems during the period could compress 4,096 x 4,096 sized images at 12 bpp to 2 bpp using lossless arithmetic coding, and over the years JPEG and JPEG2000 were imbibed reaching upto 0.1 bpp. Inspired by the reprise of deep learning based compression for natural images over the last two years, we propose a fully convolutional autoencoder for diagnostically relevant feature preserving lossy compression. This is followed by leveraging arithmetic coding for encapsulating high redundancy of features for further high-density code packing leading to variable bit length. We demonstrate performance on two different publicly available digital mammography datasets using peak signal-to-noise ratio (pSNR), structural similarity (SSIM) index and domain adaptability tests between datasets. At high density compression factors of >300x (~0.04 bpp), our approach rivals JPEG and JPEG2000 as evaluated through a Radiologist's visual Turing test.

Connected through hyperedge HE-1 (induced by source paper "fast bayesian uncertainty estimation of batch normalized single image super resolution network"):
  with members:
    - "deep learning for image super resolution a survey"
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "learning to decode 7t like mr image reconstruction from 3t mr images"
    - "residual dense network for image super resolution"
    - "densely connected pyramid dehazing network"
    - "sketch based manga retrieval using manga109 dataset"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (induced by source paper "ultracompression framework for high density compression of ultrasound volumes using physics modeling deep neural networks"):
  with members:
    - "the perception distortion tradeoff"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "simulating patho realistic ultrasound images using deep generative networks with adversarial learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "x ray image compression using convolutional recurrent neural networks"):
  with members:
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "full resolution image compression with recurrent neural networks"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)