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: "graph based inpainting for 3d dynamic point clouds"
  Abstract: With the development of depth sensors and 3D laser scanning techniques, 3D dynamic point clouds have attracted increasing attention as a format for the representation of 3D objects in motion, with applications in various fields such as 3D immersive tele-presence, navigation, animation, gaming and virtual reality. However, dynamic point clouds usually exhibit holes of missing data, mainly due to the fast motion, the limitation of acquisition techniques and complicated structure. Further, point clouds are defined on irregular non-Euclidean domain, which is challenging to address with conventional methods for regular data. Hence, leveraging on graph signal processing tools, we propose an efficient dynamic point cloud inpainting method, exploiting both the inter-frame coherence and the intra-frame self-similarity in 3D dynamic point clouds. Specifically, for each frame in a point cloud sequence, we first split it into cubes of fixed size as the processing unit, and treat cubes with holes inside as target cubes. Secondly, we take advantage of the intra-frame self-similarity in the target frame, by globally searching for the most similar cube to each target cube as the intra-source cube. Thirdly, we exploit the inter-frame coherence among every three consecutive frames, by searching the corresponding cubes in the previous and subsequent frames for each target cube as the inter-source cubes, which contains most nearest neighbors of the target cube in the relative location. Finally, we formulate dynamic point cloud inpainting as an optimization problem based on both intra- and inter-source cubes, which is regularized by the graph-signal smoothness prior. Experimental results show that the proposed approach outperforms three competing methods significantly, both in objective and subjective quality.

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.GR(Graphics)