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: "fused text segmentation networks for multi oriented scene text detection"
  Abstract: In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on total-text containing curved text which suggests effectiveness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "detecting multi oriented text with corner based region proposals"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "deep extreme cut from extreme points to object segmentation"
    - "east an efficient and accurate scene text detector"
    - "oriented response networks"
    - "extreme clicking for efficient object annotation"
    - "single shot text detector with regional attention"
    - "denet scalable real time object detection with directed sparse sampling"

Connected through hyperedge HE-2 (induced by source paper "texttubes for detecting curved text in the wild"):
  with members:
    - "inceptext a new inception text module with deformable psroi pooling for multi oriented scene text detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "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"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "sliding line point regression for shape robust scene text detection"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-3 (induced by source paper "adaptive period embedding for representing oriented objects in aerial images"):
  with members:
    - "textfield learning a deep direction field for irregular scene text detection"
    - "region proposal by guided anchoring"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "sliding line point regression for shape robust scene text detection"
    - "textboxes a single shot oriented scene text detector"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "textnet irregular text reading from images with an end to end trainable network"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"

Connected through hyperedge HE-5 (induced by source paper "fots fast oriented text spotting with a unified network"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "wordsup exploiting word annotations for character based text detection"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "sequence to label script identification for multilingual ocr"
    - "deep scene text detection with connected component proposals"
    - "deep direct regression for multi oriented scene text detection"

Connected through hyperedge HE-6 (induced by source paper "shape robust text detection with progressive scale expansion network"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "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"
    - "scene text detection with supervised pyramid context network"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "wordsup exploiting word annotations for character based text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"

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 "sliding line point regression for shape robust scene text detection"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "pagenet page boundary extraction in historical handwritten documents"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"

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)