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: "medical image denoising using convolutional denoising autoencoders"
  Abstract: Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "propagated perturbation of adversarial attack for well known cnns empirical study and its explanation"):
  with members:
    - "adversarial attacks and defences competition"
    - "stack u net refinement network for image segmentation on the example of optic disc and cup"
    - "on calibration of modern neural networks"
    - "squeeze and excitation networks"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "an approach to image denoising using manifold approximation without clean images"):
  with members:
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "noise2noise learning image restoration without clean data"
    - "correction by projection denoising images with generative adversarial networks"
    - "denoising adversarial autoencoders"
    - "deep image prior"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generative visual manipulation on the natural image manifold"

Connected through hyperedge HE-4 (induced by source paper "multi kernel prediction networks for denoising of burst images"):
  with members:
    - "compact and computationally efficient representation of deep neural networks"
    - "iterative residual cnns for burst photography applications"
    - "entropy constrained training of deep neural networks"
    - "learning to see in the dark"
    - "unprocessing images for learned raw denoising"
    - "deep burst denoising"
    - "video frame interpolation via adaptive convolution"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-5 (induced by source paper "applications of deep learning and reinforcement learning to biological data"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "brain tumor segmentation with deep neural networks"
    - "deep reinforcement learning an overview"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep learning trends for focal brain pathology segmentation in mri"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "sample efficient actor critic with experience replay"

Connected through hyperedge HE-6 (induced by source paper "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"):
  with members:
    - "self normalizing neural networks"
    - "adversarial symmetric variational autoencoder"
    - "time series compression based on adaptive piecewise recurrent autoencoder"
    - "relational autoencoder for feature extraction"
    - "autoencoder based holographic image restoration"
    - "variational autoencoders for semi supervised text classification"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "language as a latent variable discrete generative models for sentence compression"

Connected through hyperedge HE-7 (induced by source paper "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"):
  with members:
    - "classification of mri data using deep learning and gaussian process based model selection"
    - "retinal fluid segmentation and detection in optical coherence tomography images using fully convolutional neural network"
    - "relaynet retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional network"
    - "denoising of 3d magnetic resonance images with multi channel residual learning of convolutional neural network"
    - "richer convolutional features for edge detection"
    - "imagej2 imagej for the next generation of scientific image data"
    - "multiframe denoising of high speed optical coherence tomography data using interframe and intraframe priors"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "image reconstruction using deep learning"):
  with members:
    - "deep convolutional denoising of low light images"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "llnet a deep autoencoder approach to natural low light image enhancement"
    - "sparsity based poisson denoising with dictionary learning"
    - "speech recognition with deep recurrent neural networks"

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)