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: "lose the views limited angle ct reconstruction via implicit sinogram completion"
  Abstract: Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180$^\circ$ view of the object. This is impractical in a limited angle scenario, when the viewing angle is less than 180$^\circ$, which can occur due to different factors including restrictions on scanning time, limited flexibility of scanner rotation, etc. The sinograms obtained as a result, cause existing techniques to produce highly artifact-laden reconstructions. In this paper, we propose to address this problem through implicit sinogram completion, on a challenging real world dataset containing scans of common checked-in luggage. We propose a system, consisting of 1D and 2D convolutional neural networks, that operates on a limited angle sinogram to directly produce the best estimate of a reconstruction. Next, we use the x-ray transform on this reconstruction to obtain a "completed" sinogram, as if it came from a full 180$^\circ$ measurement. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by our network. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction preserves the 3D structure of objects effectively.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "mimicgan corruption mimicking for blind image recovery adversarial defense"):
  with members:
    - "universal decision based black box perturbations breaking security through obscurity defenses"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "the robust manifold defense adversarial training using generative models"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "an unsupervised approach to solving inverse problems using generative adversarial networks"):
  with members:
    - "deep image prior"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "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)