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: "image correction via deep reciprocating hdr transformation"
  Abstract: Image correction aims to adjust an input image into a visually pleasing one. Existing approaches are proposed mainly from the perspective of image pixel manipulation. They are not effective to recover the details in the under/over exposed regions. In this paper, we revisit the image formation procedure and notice that the missing details in these regions exist in the corresponding high dynamic range (HDR) data. These details are well perceived by the human eyes but diminished in the low dynamic range (LDR) domain because of the tone mapping process. Therefore, we formulate the image correction task as an HDR transformation process and propose a novel approach called Deep Reciprocating HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the missing details in the HDR domain. We then perform tone mapping on the predicted HDR data to generate the output LDR image with the recovered details. To this end, we propose a united framework consisting of two CNNs for HDR reconstruction and tone mapping. They are integrated end-to-end for joint training and prediction. Experiments on the standard benchmarks demonstrate that the proposed method performs favorably against state-of-the-art image correction methods.

Connected through hyperedge HE-1 (induced by source paper "human aware motion deblurring"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "scale recurrent network for deep image deblurring"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "deep semantic face deblurring"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "learning blind motion deblurring"

Connected through hyperedge HE-2 (induced by source paper "fhdr hdr image reconstruction from a single ldr image using feedback network"):
  with members:
    - "feedback network for image super resolution"
    - "deep chain hdri reconstructing a high dynamic range image from a single low dynamic range image"
    - "expandnet a deep convolutional neural network for high dynamic range expansion from low dynamic range content"
    - "residual dense network for image super resolution"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "learning high dynamic range from outdoor panoramas"
    - "perceptual losses for real time style transfer and super resolution"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "image enhancement network trained by using hdr images"):
  with members:
    - "a pseudo multi exposure fusion method using single image"
    - "learning to see in the dark"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "enhancing underexposed photos using perceptually bidirectional similarity"):
  with members:
    - "kindling the darkness a practical low light image enhancer"
    - "enlightengan deep light enhancement without paired supervision"
    - "deep retinex decomposition for low light enhancement"
    - "learning to see in the dark"
    - "deep bilateral learning for real time image enhancement"
    - "llnet a deep autoencoder approach to natural low light image enhancement"
    - "automatic photo adjustment using deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "joint face hallucination and deblurring via structure generation and detail enhancement"):
  with members:
    - "deep semantic face deblurring"
    - "stylizing face images via multiple exemplars"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "progressive growing of gans for improved quality stability and variation"
    - "pose invariant face alignment with a single cnn"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "dilated residual networks"

Connected through hyperedge HE-6 (induced by source paper "hybrid loss for learning single image based hdr reconstruction"):
  with members:
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "deep chain hdri reconstructing a high dynamic range image from a single low dynamic range image"
    - "expandnet a deep convolutional neural network for high dynamic range expansion from low dynamic range content"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

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)