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: "text line segmentation in compressed representation of handwritten document using tunneling algorithm"
  Abstract: Operating directly on the compressed document images without decompression would be an additional advantage for storage and transmission. In this research work, we perform text line segmentation directly in compressed representation of an unconstraint handwritten document image using tunneling algorithm. In this relation, we make use of text line terminal point which is the current state-of-the-art that enables text line segmentation. The terminal points spotted along both margins (left and right) of a document image for every text line are considered as source and target respectively. The effort in spotting the terminal positions is performed directly in the compressed domain. The tunneling algorithm uses a single agent to identify the coordinate positions in the compressed representation to perform text-line segmentation of the document. The agent starts at a source point and progressively tunnels a path routing in between two adjacent text lines and reaches the probable target. The agent’s navigation path from source to the target bypassing obstacles, if any, results in segregating the two adjacent text lines. However, the target point would be known only when the agent reaches destination; this is applicable for all source points and henceforth we could analyze the correspondence between source and target nodes. In compressed representation of a document image, the continuous pixel values in a spatial domain are available in the form of batches known as white-runs (background) and black-runs (foreground). These batches are considered as features of a document image represented in a Grid map. Performing text-line segmentation using these features makes the system inexpensive compared to spatial domain processing. Artificial Intelligence in Expert systems with dynamic programming and greedy strategies is employed for every search space for tunneling. An exhaustive experimentation is carried out on various benchmark datasets including ICDAR13 and the performances are reported.

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)