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: "line as object datasets and framework for semantic line segment detection"
  Abstract: In this work, we propose a learning-based approach to the task of detecting semantic line segments from outdoor scenes. Semantic line segments are salient edges enclosed by two endpoints on an image with apparent semantic information, e.g., the boundary between a building roof and the sky (See Fig. 1). Semantic line segments can be efficiently parameterized and fill the gap between dense feature points and sparse objects to act as an effective landmarks in applications such as large-scale High Definition Mapping (HDM). With no existing benchmarks, we have built two new datasets carefully labeled by humans that contain over 6,000 images of semantic line segments. Semantic line segments have different appearance and layout patterns that are challenging for existing object detectors. We have proposed a Semantic Line Segment Detector (SLSD) together with an unified representation and a modified evaluation metric to better detect semantic line segments. SLSD trained on our proposed datasets is shown to perform effectively and efficiently. We have conducted excessive experiments to demonstrate semantic line segment detection task as a valid and challenging research topic.

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)