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: "pcpnet learning local shape properties from raw point clouds"
  Abstract: In this paper, we propose PCPNet, a deep-learning based approach for estimating local 3D shape properties in point clouds. In contrast to the majority of prior techniques that concentrate on global or mid-level attributes, e.g., for shape classification or semantic labeling, we suggest a patch-based learning method, in which a series of local patches at multiple scales around each point is encoded in a structured manner. Our approach is especially well-adapted for estimating local shape properties such as normals (both unoriented and oriented) and curvature from raw point clouds in the presence of strong noise and multi-scale features. Our main contributions include both a novel multi-scale variant of the recently proposed PointNet architecture with emphasis on local shape information, and a series of novel applications in which we demonstrate how learning from training data arising from well-structured triangle meshes, and applying the trained model to noisy point clouds can produce superior results compared to specialized state-of-the-art techniques. Finally, we demonstrate the utility of our approach in the context of shape reconstruction, by showing how it can be used to extract normal orientation information from point clouds.

Connected through hyperedge HE-1 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"
    - "relation shape convolutional neural network for point cloud analysis"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-2 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "geometry aware generation of adversarial and cooperative point clouds"
    - "orthogonal deep neural networks"
    - "learning graph embedding with adversarial training methods"
    - "revisiting self supervised visual representation learning"
    - "part aware fine grained object categorization using weakly supervised part detection network"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-3 (induced by source paper "multimodal shape completion via conditional generative adversarial networks"):
  with members:
    - "morphing and sampling network for dense point cloud completion"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "dense 3d object reconstruction from a single depth view"
    - "unpaired point cloud completion on real scans using adversarial training"
    - "visual object networks image generation with disentangled 3d representation"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "p2p net bidirectional point displacement net for shape transform"

Connected through hyperedge HE-4 (induced by source paper "meshlet priors for 3d mesh reconstruction"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "learning category specific mesh reconstruction from image collections"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-5 (induced by source paper "deep iterative surface normal estimation"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "deep closest point learning representations for point cloud registration"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "nesti net normal estimation for unstructured 3d point clouds using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "pointfilter point cloud filtering via encoder decoder modeling"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "dynamic graph cnn for learning on point clouds"
    - "ec net an edge aware point set consolidation network"
    - "patch based progressive 3d point set upsampling"
    - "pu net point cloud upsampling network"
    - "pointsift a sift like network module for 3d point cloud semantic segmentation"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "deep learning in neural networks"

Connected through hyperedge HE-7 (induced by source paper "meshcnn a network with an edge"):
  with members:
    - "spherical cnns on unstructured grids"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "densepoint learning densely contextual representation for efficient point cloud processing"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "relation shape convolutional neural network for point cloud analysis"
    - "adaptive graph convolutional neural networks"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multiresolution tree networks for 3d point cloud processing"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"

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.CG(Computational Geometry)