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: "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
  Abstract: In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs). Deep learning has shown success in a plethora of medical image transformation problems, such as super-resolution (SR) and image synthesis. However, the highly ill-posed nature of such problems results in inevitable ambiguity in the learning of networks. We propose to account for intrinsic uncertainty through a per-patch heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference in the form of variational dropout. We show that the combined benefits of both lead to the state-of-the-art performance SR of diffusion MR brain images in terms of errors compared to ground truth. We further show that the reduced error scores produce tangible benefits in downstream tractography. In addition, the probabilistic nature of the methods naturally confers a mechanism to quantify uncertainty over the super-resolved output. We demonstrate through experiments on both healthy and pathological brains the potential utility of such an uncertainty measure in the risk assessment of the super-resolved images for subsequent clinical use.

Connected through hyperedge HE-1 (induced by source paper "simple and accurate uncertainty quantification from bias variance decomposition"):
  with members:
    - "supervised uncertainty quantification for segmentation with multiple annotations"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "reversible gans for memory efficient image to image translation"):
  with members:
    - "analyzing inverse problems with invertible neural networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "lifting layers analysis and applications"
    - "neural ordinary differential equations"
    - "i revnet deep invertible networks"
    - "deeper image quality transfer training low memory neural networks for 3d images"
    - "the reversible residual network backpropagation without storing activations"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "the intelligent icu pilot study using artificial intelligence technology for autonomous patient monitoring"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "an unsupervised learning model for deformable medical image registration"
    - "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"

Connected through hyperedge HE-4 (induced by source paper "mr image reconstruction using deep density priors"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "image reconstruction by domain transform manifold learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "tackling over pruning in variational autoencoders"
    - "deep convolutional neural network for inverse problems in imaging"
    - "a survey on deep learning in medical image analysis"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "recurrent inference machines for solving inverse problems"

Connected through hyperedge HE-5 (induced by source paper "multi stage prediction networks for data harmonization"):
  with members:
    - "spherical harmonic residual network for diffusion signal harmonization"
    - "deeper image quality transfer training low memory neural networks for 3d images"
    - "progressive growing of gans for improved quality stability and variation"
    - "convolutional pose machines"
    - "holistically nested edge detection"

Connected through hyperedge HE-6 (induced by source paper "supervised uncertainty quantification for segmentation with multiple annotations"):
  with members:
    - "highly accurate model for prediction of lung nodule malignancy with ct scans"
    - "uncertainty driven sanity check application to postoperative brain tumor cavity segmentation"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "an empirical study into annotator agreement ground truth estimation and algorithm evaluation"

Connected through hyperedge HE-7 (induced by source paper "probabilistic object detection definition and evaluation"):
  with members:
    - "evaluating merging strategies for sampling based uncertainty techniques in object detection"
    - "the limits and potentials of deep learning for robotics"
    - "yolov3 an incremental improvement"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "localization recall precision lrp a new performance metric for object detection"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-8 (induced by source paper "direct uncertainty prediction for medical second opinions"):
  with members:
    - "deep learning is robust to massive label noise"
    - "grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "smoothgrad removing noise by adding noise"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "axiomatic attribution for deep networks"
    - "who said what modeling individual labelers improves classification"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

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)