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: "deep learning for single image super resolution a brief review"
  Abstract: Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that aims to obtain a high-resolution output from one of its low-resolution versions. Recently, powerful deep learning algorithms have been applied to SISR and have achieved state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods and group them into two categories according to their contributions to two essential aspects of SISR: The exploration of efficient neural network architectures for SISR and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is first established, and several critical limitations of the baseline are summarized. Then, representative works on overcoming these limitations are presented based on their original content, as well as our critical exposition and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally, we conclude this review with some current challenges and future trends in SISR that leverage deep learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space"):
  with members:
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "an empirical investigation of randomized defenses against adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "deep learning for multiple image super resolution"
    - "sampling and super resolution of sparse signals beyond the fourier domain"
    - "deep learning for image super resolution a survey"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"

Connected through hyperedge HE-2 (induced by source paper "simultaneous enhancement and super resolution of underwater imagery for improved visual perception"):
  with members:
    - "real time model based image color correction for underwater robots"
    - "feedback network for image super resolution"
    - "fast underwater image enhancement for improved visual perception"
    - "an underwater image enhancement benchmark dataset and beyond"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "learned multi view texture super resolution"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimedia super resolution via deep learning a survey"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep network interpolation for continuous imagery effect transition"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"

Connected through hyperedge HE-4 (induced by source paper "super resolving commercial satellite imagery using realistic training data"):
  with members:
    - "camera lens super resolution"
    - "nima neural image assessment"
    - "learned perceptual image enhancement"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "super resolution for overhead imagery using densenets and adversarial learning"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "pixel level self paced learning for super resolution"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory network for image restoration"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "machine vision for improved human robot cooperation in adverse underwater conditions"):
  with members:
    - "real world underwater enhancement challenges benchmarks and solutions"
    - "fast underwater image enhancement for improved visual perception"
    - "robot to robot relative pose estimation using humans as markers"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "underwater surveying via bearing only cooperative localization"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "person following by autonomous robots a categorical overview"

Connected through hyperedge HE-7 (induced by source paper "cali sketch stroke calibration and completion for high quality face image generation from poorly drawn sketches"):
  with members:
    - "csgan cyclic synthesized generative adversarial networks for image to image transformation"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "sketchygan towards diverse and realistic sketch to image synthesis"

Connected through hyperedge HE-8 (induced by source paper "toward real world single image super resolution a new benchmark and a new model"):
  with members:
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "toward bridging the simulated to real gap benchmarking super resolution on real data"
    - "residual dense network for image super resolution"
    - "learning a single convolutional super resolution network for multiple degradations"

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)