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: "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
  Abstract: Synthesized medical images have several important applications, e.g., as an intermedium in cross-modality image registration and as supplementary training samples to boost the generalization capability of a classifier. Especially, synthesized computed tomography (CT) data can provide X-ray attenuation map for radiation therapy planning. In this work, we propose a generic cross-modality synthesis approach with the following targets: 1) synthesizing realistic looking 3D images using unpaired training data, 2) ensuring consistent anatomical structures, which could be changed by geometric distortion in cross-modality synthesis and 3) improving volume segmentation by using synthetic data for modalities with limited training samples. We show that these goals can be achieved with an end-to-end 3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks. The generators are trained with an adversarial loss, a cycle-consistency loss, and also a shape-consistency loss, which is supervised by segmentors, to reduce the geometric distortion. From the segmentation view, the segmentors are boosted by synthetic data from generators in an online manner. Generators and segmentors prompt each other alternatively in an end-to-end training fashion. With extensive experiments on a dataset including a total of 4,496 CT and magnetic resonance imaging (MRI) cardiovascular volumes, we show both tasks are beneficial to each other and coupling these two tasks results in better performance than solving them exclusively.

Connected through hyperedge HE-1 (induced by source paper "one to one mapping for unpaired image to image translation"):
  with members:
    - "towards learning a self inverse network for bidirectional image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "towards instance level image to image translation"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "analyzing inverse problems with invertible neural networks"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "attention gan for object transfiguration in wild images"

Connected through hyperedge HE-2 (induced by source paper "unpaired multi modal segmentation via knowledge distillation"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "soft labeling by distilling anatomical knowledge for improved ms lesion segmentation"
    - "group normalization"
    - "self supervised knowledge distillation using singular value decomposition"
    - "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "universal representations the missing link between faces text planktons and cat breeds"

Connected through hyperedge HE-3 (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"
    - "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"
    - "simulating patho realistic ultrasound images using deep generative networks with adversarial learning"

Connected through hyperedge HE-4 (induced by source paper "3d point cloud generative adversarial network based on tree structured graph convolutions"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "conditional image to image translation"
    - "dynamic graph cnn for learning on point clouds"
    - "vital visual tracking via adversarial learning"
    - "generative image inpainting with contextual attention"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-5 (induced by source paper "on direct distribution matching for adapting segmentation networks"):
  with members:
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "synseg net synthetic segmentation without target modality ground truth"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "a novel domain adaptation framework for medical image segmentation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "towards instance level image to image translation"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "multimodal unsupervised image to image translation"
    - "meal multi model ensemble via adversarial learning"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-7 (induced by source paper "learning a self inverse network for unpaired bidirectional image to image translation"):
  with members:
    - "towards learning a self inverse network for bidirectional image to image translation"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "towards instance level image to image translation"
    - "harmonic unpaired image to image translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "analyzing inverse problems with invertible neural networks"

Connected through hyperedge HE-8 (induced by source paper "harmonic unpaired image to image translation"):
  with members:
    - "learnable histogram statistical context features for deep neural networks"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation 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)