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 "direct processing of document images in compressed domain"):
  Abstract: Abstract: With the rapid increase in the volume of Big data of this digital era, fax documents, invoices, receipts, etc are traditionally subjected to compression for the efficiency of data storage and transfer. However, in order to process these documents, they need to undergo the stage of decompression which indents additional computing resources. This limitation induces the motivation to research on the possibility of directly processing of compressed images. In this research paper, we summarize the research work carried out to perform different operations straight from run-length compressed documents without going through the stage of decompression. The different operations demonstrated are feature extraction; text-line, word and character segmentation; document block segmentation; and font size detection, all carried out in the compressed version of the document. Feature extraction methods demonstrate how to extract the conventionally defined features such as projection profile, run-histogram and entropy, directly from the compressed document data. Document segmentation involves the extraction of compressed segments of text-lines, words and characters using the vertical and horizontal projection profile features. Further an attempt is made to segment randomly a block of interest from the compressed document and subsequently facilitate absolute and relative characterization of the segmented block which finds real time applications in automatic processing of Bank Cheques, Challans, etc, in compressed domain. Finally an application to detect font size at text line level is also investigated. All the proposed algorithms are validated experimentally with sufficient data set of compressed documents.

Includes members:
  Member "entropy computations of document images in run length compressed domain":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic detection of font size straight from run length compressed text documents")
  Member "automatic detection of font size straight from run length compressed text documents":
    also connected through other hyperedges:
      (none)
  Member "direct processing of run length compressed document image for segmentation and characterization of a specified block":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spotting separator points at line terminals in compressed document images for text line segmentation")
  Member "extraction of line word character segments directly from run length compressed printed text documents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spotting separator points at line terminals in compressed document images for text line segmentation")
  Member "extraction of projection profile run histogram and entropy features straight from run length compressed text documents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic detection of font size straight from run length compressed text documents")

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)