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: "adversarial networks for the detection of aggressive prostate cancer"
  Abstract: Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of trainable parameters of deep neural networks however renders them inherently data hungry, a characteristic that heavily challenges the medical imaging community. Though interestingly, with the de facto standard training of fully convolutional networks (FCNs) for semantic segmentation being agnostic towards the `structure' of the predicted label maps, valuable complementary information about the global quality of the segmentation lies idle. In order to tap into this potential, we propose utilizing an adversarial network which discriminates between expert and generated annotations in order to train FCNs for semantic segmentation. Because the adversary constitutes a learned parametrization of what makes a good segmentation at a global level, we hypothesize that the method holds particular advantages for segmentation tasks on complex structured, small datasets. This holds true in our experiments: We learn to segment aggressive prostate cancer utilizing MRI images of 152 patients and show that the proposed scheme is superior over the de facto standard in terms of the detection sensitivity and the dice-score for aggressive prostate cancer. The achieved relative gains are shown to be particularly pronounced in the small dataset limit.

Connected through hyperedge HE-1 (induced by source paper "boosting segmentation with weak supervision from image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "multimodal unsupervised image to image translation"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic segmentation"):
  with members:
    - "understanding deep learning techniques for image segmentation"
    - "adversarial structure matching loss for image segmentation"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "student beats the teacher deep neural networks for lateral ventricles segmentation in brain mr"
    - "efficient relaxations for dense crfs with sparse higher order potentials"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "towards semi supervised segmentation via image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "multimodal unsupervised image to image translation"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "pathology segmentation using distributional differences to images of healthy origin"

Connected through hyperedge HE-4 (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-5 (induced by source paper "segmentation of the prostatic gland and the intraprostatic lesions on multiparametic mri using mask rcnn"):
  with members:
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "feature pyramid networks for object detection"
    - "instance aware semantic segmentation via multi task network cascades"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (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"
    - "blood vessel geometry synthesis using generative adversarial networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "susan segment unannotated image structure using adversarial network"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "adversarial training and dilated convolutions for brain mri segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "3d fully convolutional networks for subcortical segmentation in mri a large scale study"

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)