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: "blood vessel geometry synthesis using generative adversarial networks"
  Abstract: Computationally synthesized blood vessels can be used for training and evaluation of medical image analysis applications. We propose a deep generative model to synthesize blood vessel geometries, with an application to coronary arteries in cardiac CT angiography (CCTA). #R##N#In the proposed method, a Wasserstein generative adversarial network (GAN) consisting of a generator and a discriminator network is trained. While the generator tries to synthesize realistic blood vessel geometries, the discriminator tries to distinguish synthesized geometries from those of real blood vessels. Both real and synthesized blood vessel geometries are parametrized as 1D signals based on the central vessel axis. The generator can optionally be provided with an attribute vector to synthesize vessels with particular characteristics. #R##N#The GAN was optimized using a reference database with parametrizations of 4,412 real coronary artery geometries extracted from CCTA scans. After training, plausible coronary artery geometries could be synthesized based on random vectors sampled from a latent space. A qualitative analysis showed strong similarities between real and synthesized coronary arteries. A detailed analysis of the latent space showed that the diversity present in coronary artery anatomy was accurately captured by the generator. #R##N#Results show that Wasserstein generative adversarial networks can be used to synthesize blood vessel geometries.

Connected through hyperedge HE-1 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks"):
  with members:
    - "medgan medical image translation using gans"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "generative adversarial network in medical imaging a review"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "deep adversarial context aware landmark detection for ultrasound imaging"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "high resolution medical image synthesis using progressively grown generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "ct gan malicious tampering of 3d medical imagery using deep learning"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "generative adversarial network in medical imaging a review"
    - "fighting fake news image splice detection via learned self consistency"
    - "melanogans high resolution skin lesion synthesis with gans"
    - "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "noiseprint a cnn based camera model fingerprint"
    - "learning implicit brain mri manifolds with deep learning"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial networks and adversarial methods in biomedical image analysis"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "high resolution medical image synthesis using progressively grown generative adversarial networks"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "domain and geometry agnostic cnns for left atrium segmentation in 3d ultrasound"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"

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)