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: "pcn point completion network"
  Abstract: Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.

Connected through hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "conditional invertible flow for point cloud generation"
    - "grip graph based interaction aware trajectory prediction"
    - "probabilistic reconstruction networks for 3d shape inference from a single image"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "human motion trajectory prediction a survey"
    - "pixor real time 3d object detection from point clouds"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-2 (induced by source paper "pf net point fractal network for 3d point cloud completion"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "relation shape convolutional neural network for point cloud analysis"
    - "rl gan net a reinforcement learning agent controlled gan network for real time point cloud shape completion"
    - "multiresolution tree networks for 3d point cloud processing"
    - "point cloud gan"
    - "triplet center loss for multi view 3d object retrieval"

Connected through hyperedge HE-3 (induced by source paper "pugeo net a geometry centric network for 3d point cloud upsampling"):
  with members:
    - "pu gan a point cloud upsampling adversarial network"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "dynamic graph cnn for learning on point clouds"
    - "residual dense network for image super resolution"
    - "ec net an edge aware point set consolidation network"
    - "pu net point cloud upsampling network"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-4 (induced by source paper "samplenet differentiable point cloud sampling"):
  with members:
    - "adaptive hierarchical down sampling for point cloud classification"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "unsupervised detection of distinctive regions on 3d shapes"
    - "logan unpaired shape transform in latent overcomplete space"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pcrnet point cloud registration network using pointnet encoding"
    - "modeling point clouds with self attention and gumbel subset sampling"

Connected through hyperedge HE-5 (induced by source paper "4d generic video object proposals"):
  with members:
    - "mots multi object tracking and segmentation"
    - "bayesian semantic instance segmentation in open set world"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combined image and world space tracking in traffic scenes"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "unsupervised hard example mining from videos for improved object detection"
    - "maskrnn instance level video object segmentation"

Connected through hyperedge HE-6 (induced by source paper "deepsdf learning continuous signed distance functions for shape representation"):
  with members:
    - "multi chart generative surface modeling"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "learning implicit fields for generative shape modeling"
    - "dynamic graph cnn for learning on point clouds"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "geodesic convolutional shape optimization"
    - "occupancy networks learning 3d reconstruction in function space"
    - "deformable shape completion with graph convolutional autoencoders"

Connected through hyperedge HE-7 (induced by source paper "dense 3d point cloud reconstruction using a deep pyramid network"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "3d psrnet part segmented 3d point cloud reconstruction from a single image"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "capnet continuous approximation projection for 3d point cloud reconstruction using 2d supervision"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-8 (induced by source paper "pu gan a point cloud upsampling adversarial network"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "ec net an edge aware point set consolidation network"
    - "patch based progressive 3d point set upsampling"
    - "high fidelity semantic shape completion for point clouds using latent optimization"
    - "pu net point cloud upsampling network"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)