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: "improving text proposals for scene images with fully convolutional networks"
  Abstract: Text Proposals have emerged as a class-dependent version of object proposals - efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text recognition. In this paper we propose an improvement over the original Text Proposals algorithm of Gomez and Karatzas (2016), combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "selective style transfer for text"):
  with members:
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "word level font to font image translation using convolutional recurrent generative adversarial networks"
    - "east an efficient and accurate scene text detector"
    - "deep photo style transfer"
    - "automatic semantic style transfer using deep convolutional neural networks and soft masks"
    - "multi content gan for few shot font style transfer"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (induced by source paper "exploring hate speech detection in multimodal publications"):
  with members:
    - "learning to learn from web data through deep semantic embeddings"
    - "recurrent fusion network for image captioning"
    - "peer to peer hate hate speech instigators and their targets"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "one step and two step classification for abusive language detection on twitter"
    - "a web of hate tackling hateful speech in online social spaces"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

Connected through hyperedge HE-3 (induced by source paper "arbitrary oriented scene text detection via rotation proposals"):
  with members:
    - "deep matching prior network toward tighter multi oriented text detection"
    - "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"
    - "text attentional convolutional neural network for scene text detection"

Connected through hyperedge HE-4 (induced by source paper "soft phoc descriptor for end to end word spotting in egocentric scene images"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "single shot text detector with regional attention"
    - "ssd single shot multibox detector"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"
    - "you only look once unified real time object detection"
    - "fully convolutional networks for semantic 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)