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: "hybrid point cloud attribute compression using slice based layered structure and block based intra prediction"
  Abstract: Point cloud compression is a key enabler for the emerging applications of immersive visual communication, autonomous driving and smart cities, etc. In this paper, we propose a hybrid point cloud attribute compression scheme built on an original layered data structure. First, a slice-partition scheme and geometry-adaptive k dimensional-tree (kd-tree) method are devised to generate the four-layer structure. Second, we introduce an efficient block-based intra prediction scheme containing a DC prediction mode and several angular modes, in order to exploit the spatial correlation between adjacent points. Third, an adaptive transform scheme based on Graph Fourier Transform (GFT) is Lagrangian optimized to achieve better transform efficiency. The Lagrange multiplier is off-line derived based on the statistics of color attribute coding. Last but not least, multiple reordering scan modes are dedicated to improve coding efficiency for entropy coding. In intra-frame compression of point cloud color attributes, results demonstrate that our method performs better than the state-of-the-art region-adaptive hierarchical transform (RAHT) system, and on average a 29.37$\%$ BD-rate gain is achieved. Comparing with the test model for category 1 (TMC1) anchor's coding results, which were recently published by MPEG-3DG group on 121st meeting, a 16.37$\%$ BD-rate gain is obtained.

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.MM(Multimedia)