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: "direct processing of run length compressed document image for segmentation and characterization of a specified block"
  Abstract: Extracting a block of interest referred to as segmenting a specified block in an image and studying its characteristics is of general research interest, and could be a challenging if such a segmentation task has to be carried out directly in a compressed image. This is the objective of the present research work. The proposal is to evolve a method which would segment and extract a specified block, and carry out its characterization without decompressing a compressed image, for two major reasons that most of the image archives contain images in compressed format and ‘decompressing’ an image indents additional computing time and space. Specifically in this research work, the proposal is to work on run-length compressed document images.

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:
    - "extraction of line word character segments directly from run length compressed printed text documents"

Connected through hyperedge HE-2 (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"
    - "extraction of line word character segments directly from run length compressed printed text documents"
    - "extraction of projection profile run histogram and entropy features straight from run length compressed text documents"

Connected through hyperedge HE-3 (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"
    - "extraction of line word character segments directly from run length compressed printed text documents"
    - "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)