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: "a deep journey into super resolution a survey"
  Abstract: Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications. In this exposition, we extensively compare 30+ state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution. We introduce a taxonomy for deep-learning based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based and adversarial designs. We also provide comparisons between the models in terms of network complexity, memory footprint, model input and output, learning details, the type of network losses and important architectural differences (e.g., depth, skip-connections, filters). The extensive evaluation performed, shows the consistent and rapid growth in the accuracy in the past few years along with a corresponding boost in model complexity and the availability of large-scale datasets. It is also observed that the pioneering methods identified as the benchmark have been significantly outperformed by the current contenders. Despite the progress in recent years, we identify several shortcomings of existing techniques and provide future research directions towards the solution of these open problems.

Connected through hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "kindling the darkness a practical low light image enhancer"
    - "residual non local attention networks for image restoration"
    - "making convolutional networks shift invariant again"
    - "selective kernel networks"
    - "deep learning for image super resolution a survey"
    - "real image denoising with feature attention"
    - "deep high resolution representation learning for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning"):
  with members:
    - "deceiving image to image translation networks for autonomous driving with adversarial perturbations"
    - "events to video bringing modern computer vision to event cameras"
    - "end to end learning of representations for asynchronous event based data"
    - "focus is all you need loss functions for event based vision"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "the relativistic discriminator a key element missing from standard gan"
    - "unsupervised event based learning of optical flow depth and egomotion"

Connected through hyperedge HE-3 (induced by source paper "learned image downscaling for upscaling using content adaptive resampler"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "image super resolution using very deep residual channel attention networks"
    - "ram residual attention module for single image super resolution"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-4 (induced by source paper "tomogan low dose synchrotron x ray tomography with generative adversarial networks"):
  with members:
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep image prior"
    - "deep convolutional neural network for inverse problems in imaging"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "tomogan low dose x ray tomography with generative adversarial networks"):
  with members:
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep convolutional neural network for inverse problems in imaging"
    - "improved training of wasserstein gans"
    - "lose the views limited angle ct reconstruction via implicit sinogram completion"

Connected through hyperedge HE-6 (induced by source paper "training set effect on super resolution for automated target recognition"):
  with members:
    - "on training deep networks for satellite image super resolution"
    - "deep learning for single image super resolution a brief review"
    - "srgan training dataset matters"
    - "yolov3 an incremental improvement"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "mask r cnn"
    - "perceptual losses for real time style transfer and super resolution"

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)