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: "textproposals a text specific selective search algorithm for word spotting in the wild"
  Abstract: Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way. #R##N#Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10 percent f-score the best-performing method in the last ICDAR Robust Reading Competition. Source code of the complete end-to-end system is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "integrating scene text and visual appearance for fine grained image classification"):
  with members:
    - "attention based multimodal fusion for video description"
    - "text flow a unified text detection system in natural scene images"
    - "image captioning with semantic attention"
    - "ssd single shot multibox detector"
    - "weakly supervised fine grained categorization with part based image representation"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"
    - "deeptext a unified framework for text proposal generation and text detection in natural images"

Connected through hyperedge HE-2 (induced by source paper "stn ocr a single neural network for text detection and text recognition"):
  with members:
    - "synthetic data for text localisation in natural images"
    - "recurrent spatial transformer networks"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "spatial transformer networks"
    - "reading scene text in deep convolutional sequences"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "text extraction from texture images using masked signal decomposition"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "text flow a unified text detection system in natural scene images"
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "a hough transform based technique for text segmentation"

Connected through hyperedge HE-4 (induced by source paper "improving text proposals for scene images with fully convolutional networks"):
  with members:
    - "robust text detection in natural scene images"
    - "characterness an indicator of text in the wild"

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)