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: "learning interpretable anatomical features through deep generative models application to cardiac remodeling"
  Abstract: Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical diagnosis. In this work, we propose a 3D convolutional generative model for automatic classification of images from patients with cardiac diseases associated with structural remodeling. The model leverages interpretable task-specific anatomic patterns learned from 3D segmentations. It further allows to visualise and quantify the learned pathology-specific remodeling patterns in the original input space of the images. This approach yields high accuracy in the categorization of healthy and hypertrophic cardiomyopathy subjects when tested on unseen MR images from our own multi-centre dataset (100%) as well on the ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning approach is a promising step towards the development of interpretable classifiers for the medical imaging domain, which may help clinicians to improve diagnostic accuracy and enhance patient risk-stratification.

Connected through hyperedge HE-1 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "an evaluation of the human interpretability of explanation"
    - "protoattend attention based prototypical learning"
    - "understanding the un interpretability of natural image distributions using generative models"
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "interpretable dynamics models for data efficient reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "deep learning in cardiology"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor type classification via capsule networks"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"

Connected through hyperedge HE-3 (induced by source paper "learning a probabilistic model for diffeomorphic registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "generative adversarial networks for mr ct deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "birnet brain image registration using dual supervised fully convolutional networks"
    - "learning myelin content in multiple sclerosis from multimodal mri through adversarial training"
    - "an unsupervised learning model for deformable medical image registration"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"
    - "dual motion gan for future flow embedded video prediction"

Connected through hyperedge HE-4 (induced by source paper "learning interpretable features via adversarially robust optimization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "learning deep features for discriminative localization"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-5 (induced by source paper "deep learning cardiac motion analysis for human survival prediction"):
  with members:
    - "automatic 3d bi ventricular segmentation of cardiac images by a shape constrained multi task deep learning approach"
    - "deep representation learning for human motion prediction and classification"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "discriminative recurrent sparse auto encoders"

Connected through hyperedge HE-6 (induced by source paper "assessing the impact of blood pressure on cardiac function using interpretable biomarkers and variational autoencoders"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "3d high resolution cardiac segmentation reconstruction from 2d views using conditional variational autoencoders"):
  with members:
    - "learning a predictable and generative vector representation for objects"

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)