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: "extraction of line word character segments directly from run length compressed printed text documents"
  Abstract: Segmentation of a text-document into lines, words and characters, which is considered to be the crucial preprocessing stage in Optical Character Recognition (OCR) is traditionally carried out on uncompressed documents, although most of the documents in real life are available in compressed form, for the reasons such as transmission and storage efficiency. However, this implies that the compressed image should be decompressed, which indents additional computing resources. This limitation has motivated us to take up research in document image analysis using compressed documents. In this paper, we think in a new way to carry out segmentation at line, word and character level in run-length compressed printed-text-documents. We extract the horizontal projection profile curve from the compressed file and using the local minima points perform line segmentation. However, tracing vertical information which leads to tracking words-characters in a run-length compressed file is not very straight forward. Therefore, we propose a novel technique for carrying out simultaneous word and character segmentation by popping out column runs from each row in an intelligent sequence. The proposed algorithms have been validated with 1101 text-lines, 1409 words and 7582 characters from a data-set of 35 noise and skew free compressed documents of Bengali, Kannada and English Scripts.

Connected through hyperedge HE-1 (induced by source paper "spotting separator points at line terminals in compressed document images for text line segmentation"):
  with members:
    - "direct processing of run length compressed document image for segmentation and characterization of a specified block"

Connected through hyperedge HE-2 (induced by source paper "a bottom up procedure for text line segmentation of latin script"):
  with members:
    - "text line segmentation of historical documents a survey"

Connected through hyperedge HE-3 (induced by source paper "direct processing of document images in compressed domain"):
  with members:
    - "entropy computations of document images in run length compressed domain"
    - "automatic detection of font size straight from run length compressed text documents"
    - "direct processing of run length compressed document image for segmentation and characterization of a specified block"
    - "extraction of projection profile run histogram and entropy features straight from run length compressed text documents"

Connected through hyperedge HE-4 (induced by source paper "automatic detection of font size straight from run length compressed text documents"):
  with members:
    - "entropy computations of document images in run length compressed domain"
    - "direct processing of run length compressed document image for segmentation and characterization of a specified block"
    - "extraction of projection profile run histogram and entropy features straight from run length compressed text documents"

Connected through hyperedge HE-5 (induced by source paper "direct processing of run length compressed document image for segmentation and characterization of a specified block"):
  with members:
    - "entropy computations of document images in run length compressed domain"
    - "extraction of projection profile run histogram and entropy features straight from run length compressed text documents"

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)