human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "word searching in scene image and video frame in multi script scenario using dynamic shape coding"):
  Abstract: Retrieval of text information from natural scene images and video frames is a challenging task due to its inherent problems like complex character shapes, low resolution, background noise, etc. Available OCR systems often fail to retrieve such information in scene/video frames. Keyword spotting, an alternative way to retrieve information, performs efficient text searching in such scenarios. However, current word spotting techniques in scene/video images are script-specific and they are mainly developed for Latin script. This paper presents a novel word spotting framework using dynamic shape coding for text retrieval in natural scene image and video frames. The framework is designed to search query keyword from multiple scripts with the help of on-the-fly script-wise keyword generation for the corresponding script. We have used a two-stage word spotting approach using Hidden Markov Model (HMM) to detect the translated keyword in a given text line by identifying the script of the line. A novel unsupervised dynamic shape coding based scheme has been used to group similar shape characters to avoid confusion and to improve text alignment. Next, the hypotheses locations are verified to improve retrieval performance. To evaluate the proposed system for searching keyword from natural scene image and video frames, we have considered two popular Indic scripts such as Bangla (Bengali) and Devanagari along with English. Inspired by the zone-wise recognition approach in Indic scripts[1], zone-wise text information has been used to improve the traditional word spotting performance in Indic scripts. For our experiment, a dataset consisting of images of different scenes and video frames of English, Bangla and Devanagari scripts were considered. The results obtained showed the effectiveness of our proposed word spotting approach.

Includes members:
  Member "date field retrieval in scene image and video frames using text enhancement and shape coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi oriented text detection and verification in video frames and scene images")
  Member "multi oriented text detection and verification in video frames and scene images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive embedding gate for attention based scene text recognition")
  Member "text recognition in scene image and video frame using color channel selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi oriented text detection and verification in video frames and scene images")
  Member "cross language framework for word recognition and spotting of indic scripts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning")
  Member "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multi oriented chinese keyword spotter guided by text line detection")
  Member "hmm based indic handwritten word recognition using zone segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "handwriting recognition in low resource scripts using adversarial learning")
  Member "phocnet a deep convolutional neural network for word spotting in handwritten documents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "annotation free learning of deep representations for word spotting using synthetic data and self labeling")
  Member "reading scene text in deep convolutional sequences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scene text recognition via transformer")

Question: Please tell me which class the source paper 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)