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: "crrn multi scale guided concurrent reflection removal network"
  Abstract: Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle this problem in a unified framework. Our proposed network integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Extensive experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "single image reflection removal exploiting misaligned training data and network enhancements"):
  with members:
    - "generative single image reflection separation"
    - "group normalization"
    - "the relativistic discriminator a key element missing from standard gan"
    - "single image reflection removal using deep encoder decoder network"
    - "single image reflection separation with perceptual losses"
    - "the contextual loss for image transformation with non aligned data"
    - "context encoding for semantic segmentation"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-2 (induced by source paper "face image reflection removal"):
  with members:
    - "video enhancement with task oriented flow"
    - "single image reflection separation with perceptual losses"
    - "learning warped guidance for blind face restoration"
    - "deep semantic face deblurring"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "is second order information helpful for large scale visual recognition"

Connected through hyperedge HE-3 (induced by source paper "deep reflection prior"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "fast single image reflection suppression via convex optimization"
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "single image reflection separation with perceptual losses"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "reflection separation using guided annotation"
    - "a generic deep architecture for single image reflection removal and image smoothing"

Connected through hyperedge HE-4 (induced by source paper "single image reflection removal through cascaded refinement"):
  with members:
    - "single image reflection removal exploiting misaligned training data and network enhancements"
    - "generative single image reflection separation"
    - "single image reflection removal using deep encoder decoder network"
    - "single image reflection separation with perceptual losses"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "reflection separation using guided annotation"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "hidden community detection in social networks"

Connected through hyperedge HE-5 (induced by source paper "single image reflection removal with physically based rendering"):
  with members:
    - "single image reflection separation with perceptual losses"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "single image depth perception in the wild"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-6 (induced by source paper "semantic guided single image reflection removal"):
  with members:
    - "single image reflection separation with perceptual losses"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "rethinking atrous convolution for semantic image segmentation"
    - "separating reflection and transmission images in the wild"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "robust optical flow estimation of double layer images under transparency or reflection"

Connected through hyperedge HE-7 (induced by source paper "mirror mirror on the wall who s got the clearest image of them all a tailored approach to single image reflection removal"):
  with members:
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "adam a method for stochastic optimization"

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)