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: "characterness an indicator of text in the wild"
  Abstract: Text in an image provides vital information for interpreting its contents, and text in a scene can aide with a variety of tasks from navigation, to obstacle avoidance, and odometry. Despite its value, however, identifying general text in images remains a challenging research problem. Motivated by the need to consider the widely varying forms of natural text, we propose a bottom-up approach to the problem which reflects the `characterness' of an image region. In this sense our approach mirrors the move from saliency detection methods to measures of `objectness'. In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration. Because text is made up of sets of characters, we then design a Markov random field (MRF) model so as to exploit the inherent dependencies between characters. #R##N#We experimentally demonstrate the effectiveness of our characterness cues as well as the advantage of Bayesian multi-cue integration. The proposed text detector outperforms state-of-the-art methods on a few benchmark scene text detection datasets. We also show that our measurement of `characterness' is superior than state-of-the-art saliency detection models when applied to the same task.

Connected through hyperedge HE-1 (induced by source paper "textfield learning a deep direction field for irregular scene text detection"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "an end to end textspotter with explicit alignment and attention"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "deepflux for skeletons in the wild"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "textboxes a single shot oriented scene text detector"
    - "learning markov clustering networks for scene text detection"

Connected through hyperedge HE-2 (induced by source paper "textboxes a single shot oriented scene text detector"):
  with members:
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "detecting oriented text in natural images by linking segments"
    - "text attentional convolutional neural network for scene text detection"
    - "ssd single shot multibox detector"
    - "scene text detection via holistic multi channel prediction"
    - "pvanet deep but lightweight neural networks for real time object detection"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"

Connected through hyperedge HE-3 (induced by source paper "wordsup exploiting word annotations for character based text detection"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "east an efficient and accurate scene text detector"
    - "detecting oriented text in natural images by linking segments"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "improving text proposals for scene images with fully convolutional networks"):
  with members:
    - "textproposals a text specific selective search algorithm for word spotting in the wild"
    - "robust text detection in natural scene images"

Connected through hyperedge HE-5 (induced by source paper "enhanced characterness for text detection in the wild"):
  with members:
    - "text attentional convolutional neural network for scene text detection"
    - "reading text in the wild with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "text attentional convolutional neural network for scene text detection"):
  with members:
    - "learning deep representation for face alignment with auxiliary attributes"
    - "synthetic data for text localisation in natural images"
    - "reading scene text in deep convolutional sequences"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "reading text in the wild with convolutional neural networks"
    - "robust text detection in natural scene images"

Connected through hyperedge HE-7 (induced by source paper "in the saddle chasing fast and repeatable features"):
  with members:
    - "wxbs wide baseline stereo generalizations"
    - "mods fast and robust method for two view matching"
    - "tilde a temporally invariant learned detector"

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)