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: "scene text eraser"
  Abstract: The character information in natural scene images contains various personal information, such as telephone numbers, home addresses, etc. It is a high risk of leakage the information if they are published. In this paper, we proposed a scene text erasing method to properly hide the information via an inpainting convolutional neural network (CNN) model. The input is a scene text image, and the output is expected to be text erased image with all the character regions filled up the colors of the surrounding background pixels. This work is accomplished by a CNN model through convolution to deconvolution with interconnection process. The training samples and the corresponding inpainting images are considered as teaching signals for training. To evaluate the text erasing performance, the output images are detected by a novel scene text detection method. Subsequently, the same measurement on text detection is utilized for testing the images in benchmark dataset ICDAR2013. Compared with direct text detection way, the scene text erasing process demonstrates a drastically decrease on the precision, recall and f-score. That proves the effectiveness of proposed method for erasing the text in natural scene images.

Connected through hyperedge HE-1 (induced by source paper "editing text in the wild"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "stefann scene text editor using font adaptive neural network"
    - "synthesizing images of humans in unseen poses"
    - "spectral normalization for generative adversarial networks"
    - "tet gan text effects transfer via stylization and destylization"
    - "scene text detection and recognition the deep learning era"
    - "ensnet ensconce text in the wild"

Connected through hyperedge HE-2 (induced by source paper "image inpainting a review"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "a novel approach for robust multi human action detection and recognition based on 3 dimentional convolutional neural networks"
    - "blind visual motif removal from a single image"
    - "adversarial inpainting of medical image modalities"
    - "deep hyperspectral prior denoising inpainting super resolution"
    - "pepsi fast and lightweight network for image inpainting"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"

Connected through hyperedge HE-3 (induced by source paper "scene text magnifier"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "pixellink detecting scene text via instance segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "textboxes a single shot oriented scene text detector"
    - "scene text detection and recognition the deep learning era"
    - "ensnet ensconce text in the wild"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-4 (induced by source paper "mtrnet a generic scene text eraser"):
  with members:
    - "component based attention for large scale trademark retrieval"
    - "ensnet ensconce text in the wild"
    - "east an efficient and accurate scene text detector"
    - "detecting curve text in the wild new dataset and new solution"
    - "image to image translation with conditional adversarial networks"
    - "synthetic data for text localisation in natural images"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "component based attention for large scale trademark retrieval"):
  with members:
    - "improved person re identification based on saliency and semantic parsing with deep neural network models"
    - "focal loss for dense object detection"
    - "selective deep convolutional features for image retrieval"
    - "a large scale dataset and benchmark for similar trademark retrieval"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "good practice in cnn feature transfer"
    - "detecting text in natural image with connectionist text proposal network"

Connected through hyperedge HE-6 (induced by source paper "multi scale cnn stereo and pattern removal technique for underwater active stereo system"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "fast trainable multiscale denoising"
    - "benchmarking single image dehazing and beyond"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-7 (induced by source paper "ensnet ensconce text in the wild"):
  with members:
    - "dssd deconvolutional single shot detector"
    - "convolutional neural pyramid for image processing"
    - "image de raining using a conditional generative adversarial network"
    - "perceptual losses for real time style transfer and super resolution"
    - "accelerating the super resolution convolutional neural network"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "synthetic data for text localisation in natural images"
    - "a neural algorithm of artistic style"

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)