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 compression of dynamic 3d point cloud sequences"
  Abstract: This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames share some similarities, motion estimation is key to effective compression of these sequences. It, however, remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the point clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature-matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring a significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way.

Connected through hyperedge HE-1 (induced by source paper "region adaptive graph fourier transform for 3d point clouds"):
  with members:
    - "subgraph based filterbanks for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-2 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d point capsule networks"
    - "weighted multi projection 3d point cloud denoising with estimated tangent planes"
    - "a graph cnn for 3d point cloud classification"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-3 (induced by source paper "sampling of graph signals via randomized local aggregations"):
  with members:
    - "greedy sampling of graph signals"
    - "the dual graph shift operator identifying the support of the frequency domain"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "random sampling of bandlimited signals on graphs"
    - "discrete signal processing on graphs sampling theory"
    - "gspbox a toolbox for signal processing on graphs"

Connected through hyperedge HE-4 (induced by source paper "learned point cloud geometry compression"):
  with members:
    - "learning convolutional transforms for lossy point cloud geometry compression"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "learning representations and generative models for 3d point clouds"
    - "end to end optimized image compression"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generative and discriminative voxel modeling with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning convolutional transforms for lossy point cloud geometry compression"):
  with members:
    - "focal loss for dense object detection"
    - "learning representations and generative models for 3d point clouds"
    - "learning a predictable and generative vector representation for objects"
    - "end to end optimized image compression"
    - "orientation boosted voxel nets for 3d object recognition"
    - "adam a method for stochastic optimization"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-6 (induced by source paper "graph based inpainting for 3d dynamic point clouds"):
  with members:
    - "local frequency interpretation and non local self similarity on graph for point cloud inpainting"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "blind image deblurring via reweighted graph total variation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-7 (induced by source paper "predictive generalized graph fourier transform for attribute compression of dynamic point clouds"):
  with members:
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "graph learning from data under laplacian and structural constraints"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-8 (induced by source paper "holocast graph signal processing for graceful point cloud delivery"):
  with members:
    - "soccer on your tabletop"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "dynamic polygon clouds representation and compression for vr ar"

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)