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: "simultaneous super resolution and cross modality synthesis of 3d medical images using weakly supervised joint convolutional sparse coding"
  Abstract: Magnetic Resonance Imaging (MRI) offers high-resolution \emph{in vivo} imaging and rich functional and anatomical multimodality tissue contrast. In practice, however, there are challenges associated with considerations of scanning costs, patient comfort, and scanning time that constrain how much data can be acquired in clinical or research studies. In this paper, we explore the possibility of generating high-resolution and multimodal images from low-resolution single-modality imagery. We propose the weakly-supervised joint convolutional sparse coding to simultaneously solve the problems of super-resolution (SR) and cross-modality image synthesis. The learning process requires only a few registered multimodal image pairs as the training set. Additionally, the quality of the joint dictionary learning can be improved using a larger set of unpaired images. To combine unpaired data from different image resolutions/modalities, a hetero-domain image alignment term is proposed. Local image neighborhoods are naturally preserved by operating on the whole image domain (as opposed to image patches) and using joint convolutional sparse coding. The paired images are enhanced in the joint learning process with unpaired data and an additional maximum mean discrepancy term, which minimizes the dissimilarity between their feature distributions. Experiments show that the proposed method outperforms state-of-the-art techniques on both SR reconstruction and simultaneous SR and cross-modality synthesis.

Connected through hyperedge HE-1 (induced by source paper "hi net hybrid fusion network for multi modal mr image synthesis"):
  with members:
    - "skrgan sketching rendering unconditional generative adversarial networks for medical image synthesis"
    - "generative adversarial network in medical imaging a review"
    - "brain tumor segmentation on mri with missing modalities"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "generative adversarial training for mra image synthesis using multi contrast mri"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "towards adversarial retinal image synthesis"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural networks with intermediate loss for 3d super resolution of ct and mri scans"):
  with members:
    - "super resolution of brain mri images using overcomplete dictionaries and nonlocal similarity"
    - "a tensor factorization method for 3 d super resolution with application to dental ct"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "image super resolution using very deep residual channel attention networks"
    - "channel splitting network for single mr image super resolution"
    - "lstm fully convolutional networks for time series classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-3 (induced by source paper "towards learning a self inverse network for bidirectional image to image translation"):
  with members:
    - "towards instance level image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "analyzing inverse problems with invertible neural networks"
    - "multimodal unsupervised image to image translation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "i revnet deep invertible networks"
    - "drit diverse image to image translation via disentangled representations"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "deeply supervised depth map super resolution as novel view synthesis"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep depth super resolution learning depth super resolution using deep convolutional neural network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "a deep primal dual network for guided depth super resolution"
    - "enhancenet single image super resolution through automated texture synthesis"

Connected through hyperedge HE-5 (induced by source paper "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "densely connected pyramid dehazing network"
    - "began boundary equilibrium generative adversarial networks"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "towards adversarial retinal image synthesis"
    - "learning compositional visual concepts with mutual consistency"
    - "gans for biological image synthesis"

Connected through hyperedge HE-6 (induced by source paper "image synthesis in multi contrast mri with conditional generative adversarial networks"):
  with members:
    - "an introduction to image synthesis with generative adversarial nets"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "synthesis of positron emission tomography pet images via multi channel generative adversarial networks gans"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"
    - "virtual pet images from ct data using deep convolutional networks initial results"

Connected through hyperedge HE-7 (induced by source paper "synseg net synthetic segmentation without target modality ground truth"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "towards adversarial retinal image synthesis"
    - "gans for biological image synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"

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)