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: "ensnet ensconce text in the wild"
  Abstract: A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background. The latter is a novel local-sensitive GAN, which attentively assesses the local consistency of the text erased regions. Both qualitative and quantitative sensitivity experiments on synthetic images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet is essential to achieve a good performance. Moreover, our EnsNet can significantly outperform previous state-of-the-art methods in terms of all metrics. In addition, a qualitative experiment conducted on the SMBNet dataset further demonstrates that the proposed method can also preform well on general object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can preform at 333 fps on an i5-8600 CPU device.

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"
    - "east an efficient and accurate scene text detector"

Connected through hyperedge HE-2 (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"
    - "scene text eraser"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-3 (induced by source paper "mtrnet a generic scene text eraser"):
  with members:
    - "component based attention for large scale trademark retrieval"
    - "east an efficient and accurate scene text detector"
    - "detecting curve text in the wild new dataset and new solution"
    - "scene text eraser"
    - "image to image translation with conditional adversarial networks"
    - "synthetic data for text localisation in natural images"
    - "u net convolutional networks for biomedical image segmentation"

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)