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: "accurate text localization in natural image with cascaded convolutional text network"
  Abstract: We introduce a new top-down pipeline for scene text detection. We propose a novel Cascaded Convolutional Text Network (CCTN) that joints two customized convolutional networks for coarse-to-fine text localization. The CCTN fast detects text regions roughly from a low-resolution image, and then accurately localizes text lines from each enlarged region. We cast previous character based detection into direct text region estimation, avoiding multiple bottom- up post-processing steps. It exhibits surprising robustness and discriminative power by considering whole text region as detection object which provides strong semantic information. We customize convolutional network by develop- ing rectangle convolutions and multiple in-network fusions. This enables it to handle multi-shape and multi-scale text efficiently. Furthermore, the CCTN is computationally efficient by sharing convolutional computations, and high-level property allows it to be invariant to various languages and multiple orientations. It achieves 0.84 and 0.86 F-measures on the ICDAR 2011 and ICDAR 2013, delivering substantial improvements over state-of-the-art results [23, 1].

Connected through hyperedge HE-1 (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-2 (induced by source paper "character region awareness for text detection"):
  with members:
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "textboxes a single shot oriented scene text detector"
    - "nsml meet the mlaas platform with a real world case study"
    - "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"

Connected through hyperedge HE-3 (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"
    - "wordfence text detection in natural images with border awareness"
    - "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"

Connected through hyperedge HE-4 (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"
    - "wordfence text detection in natural images with border awareness"
    - "single shot text detector with regional attention"

Connected through hyperedge HE-5 (induced by source paper "an end to end textspotter with explicit alignment and attention"):
  with members:
    - "fots fast oriented text spotting with a unified network"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "wetext scene text detection under weak supervision"
    - "deep direct regression for multi oriented scene text detection"
    - "text flow a unified text detection system in natural scene images"
    - "text attentional convolutional neural network for scene text detection"

Connected through hyperedge HE-6 (induced by source paper "verisimilar image synthesis for accurate detection and recognition of texts in scenes"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "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"
    - "wetext scene text detection under weak supervision"
    - "detecting oriented text in natural images by linking segments"
    - "text flow a unified text detection system in natural scene images"

Connected through hyperedge HE-7 (induced by source paper "textmountain accurate scene text detection via instance segmentation"):
  with members:
    - "shape robust text detection with progressive scale expansion network"
    - "weakly supervised instance segmentation using class peak response"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "pixellink detecting scene text via instance segmentation"
    - "unified perceptual parsing for scene understanding"
    - "sliding line point regression for shape robust scene text detection"
    - "textboxes a single shot oriented scene text detector"
    - "semi convolutional operators for instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "arbitrary oriented scene text detection via rotation proposals"):
  with members:
    - "deep matching prior network toward tighter multi oriented text detection"
    - "improving text proposals for scene images with fully convolutional networks"
    - "cascaded segmentation detection networks for word level text spotting"
    - "integrating scene text and visual appearance for fine grained image classification"
    - "detecting oriented text in natural images by linking segments"
    - "evolving boxes for fast vehicle detection"
    - "text flow a unified text detection system in natural scene images"
    - "character proposal network for robust text extraction"

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)