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: "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
  Abstract: Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task. State-of-the-art approaches are mostly based on supervised learning making use of large annotated datasets. Human beings, on the other hand, even non-experts, can detect most abnormal lesions after seeing a handful of healthy brain images. Replicating this capability of using prior information on the appearance of healthy brain structure to detect lesions can help computers achieve human level abnormality detection, specifically reducing the need for numerous labeled examples and bettering generalization of previously unseen lesions. To this end, we study detection of lesion regions in an unsupervised manner by learning data distribution of brain MRI of healthy subjects using auto-encoder based methods. We hypothesize that one of the main limitations of the current models is the lack of consistency in latent representation. We propose a simple yet effective constraint that helps mapping of an image bearing lesion close to its corresponding healthy image in the latent space. We use the Human Connectome Project dataset to learn distribution of healthy-appearing brain MRI and report improved detection, in terms of AUC, of the lesions in the BRATS challenge dataset.

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"
    - "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"
    - "pathology segmentation using distributional differences to images of healthy origin"

Connected through hyperedge HE-2 (induced by source paper "robust image segmentation quality assessment without ground truth"):
  with members:
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "real time prediction of segmentation quality"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "identifying and categorizing anomalies in retinal imaging data"
    - "adversarial examples in the physical world"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised anomaly localization using variational auto encoders"):
  with members:
    - "diagnosing and enhancing vae models"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep generative models in the real world an open challenge from medical imaging"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "and autoregressive novelty detectors"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "adversarial pseudo healthy synthesis needs pathology factorization"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "synseg net synthetic segmentation without target modality ground truth"
    - "unsupervised learning for cross domain medical image synthesis using deformation invariant cycle consistency networks"
    - "an adversarial learning approach to medical image synthesis for lesion removal"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "anomaly detection with adversarial dual autoencoders"):
  with members:
    - "generative adversarial interpolative autoencoding adversarial training on latent space interpolations encourages convex latent distributions"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "an adversarial learning approach to medical image synthesis for lesion removal"):
  with members:
    - "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"
    - "3d human pose estimation in the wild by adversarial learning"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"

Connected through hyperedge HE-7 (induced by source paper "deep generative models in the real world an open challenge from medical imaging"):
  with members:
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "isolating sources of disentanglement in variational autoencoders"
    - "progressive growing of gans for improved quality stability and variation"
    - "adversarial discriminative domain adaptation"
    - "improved training of wasserstein gans"
    - "semantic image inpainting with deep generative models"
    - "semantic image inpainting with perceptual and contextual losses"
    - "importance weighted autoencoders"

Connected through hyperedge HE-8 (induced by source paper "context encoding variational autoencoder for unsupervised anomaly detection"):
  with members:
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep generative models in the real world an open challenge from medical imaging"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "context encoders feature learning by inpainting"
    - "striving for simplicity the all convolutional net"

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)