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: "wordfence text detection in natural images with border awareness"
  Abstract: In recent years, text recognition has achieved remarkable success in recognizing scanned document text. However, word recognition in natural images is still an open problem, which generally requires time consuming post-processing steps. We present a novel architecture for individual word detection in scene images based on semantic segmentation. Our contributions are twofold: the concept of WordFence, which detects border areas surrounding each individual word and a novel pixelwise weighted softmax loss function which penalizes background and emphasizes small text regions. WordFence ensures that each word is detected individually, and the new loss function provides a strong training signal to both text and word border localization. The proposed technique avoids intensive post-processing, producing an end-to-end word detection system. We achieve superior localization recall on common benchmark datasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT. Furthermore, our end-to-end word recognition system achieves state-of-the-art 86% F-Score on ICDAR13.

Connected through hyperedge HE-1 (induced by source paper "ga dan geometry aware domain adaptation network for scene text detection and recognition"):
  with members:
    - "adaptive composition gan towards realistic image synthesis"
    - "scene text synthesis for efficient and effective deep network training"
    - "rotation sensitive regression for oriented scene text detection"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "fots fast oriented text spotting with a unified network"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"

Connected through hyperedge HE-2 (induced by source paper "it s all about the scale efficient text detection using adaptive scaling"):
  with members:
    - "character region awareness for text detection"
    - "adascale towards real time video object detection using adaptive scaling"
    - "pyramid mask text detector"
    - "shape robust text detection with progressive scale expansion network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "autofocus efficient multi scale inference"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"

Connected through hyperedge HE-3 (induced by source paper "msr multi scale shape regression for scene text detection"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "textboxes a single shot oriented scene text detector"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "wordsup exploiting word annotations for character based text detection"

Connected through hyperedge HE-4 (induced by source paper "a pooling based scene text proposal technique for scene text reading in the wild"):
  with members:
    - "an end to end textspotter with explicit alignment and attention"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "icdar2017 competition on reading chinese text in the wild rctw 17"
    - "wetext scene text detection under weak supervision"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "detecting oriented text in natural images by linking segments"

Connected through hyperedge HE-5 (induced by source paper "accurate scene text detection through border semantics awareness and bootstrapping"):
  with members:
    - "an end to end textspotter with explicit alignment and attention"
    - "learning markov clustering networks for scene text detection"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "random erasing data augmentation"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "icdar2017 competition on reading chinese text in the wild rctw 17"

Connected through hyperedge HE-6 (induced by source paper "wetext scene text detection under weak supervision"):
  with members:
    - "end to end interpretation of the french street name signs dataset"
    - "text flow a unified text detection system in natural scene images"
    - "text attentional convolutional neural network for scene text detection"
    - "multi oriented text detection with fully convolutional networks"
    - "ssd single shot multibox detector"
    - "accurate text localization in natural image with cascaded convolutional text network"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural 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)