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: "pointcleannet learning to denoise and remove outliers from dense point clouds"
  Abstract: Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising largely rely on local surface fitting (e.g., jets or MLS surfaces), local or non-local averaging, or on statistical assumptions about the underlying noise model. In contrast, we develop a simple data-driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds. Our method first classifies and discards outlier samples, and then estimates correction vectors that project noisy points onto the original clean surfaces. The approach is efficient and robust to varying amounts of noise and outliers, while being able to handle large densely-sampled point clouds. In our extensive evaluation, both on synthesic and real data, we show an increased robustness to strong noise levels compared to various state-of-the-art methods, enabling accurate surface reconstruction from extremely noisy real data obtained by range scans. Finally, the simplicity and universality of our approach makes it very easy to integrate in any existing geometry processing pipeline.

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"
    - "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"
    - "point convolutional neural networks by extension operators"

Connected through hyperedge HE-2 (induced by source paper "non local part aware point cloud denoising"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "kpconv flexible and deformable convolution for point clouds"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "pu gan a point cloud upsampling adversarial network"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-3 (induced by source paper "differentiable surface splatting for point based geometry processing"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "inverse path tracing for joint material and lighting estimation"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "3d aware scene manipulation via inverse graphics"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "ec net an edge aware point set consolidation network"

Connected through hyperedge HE-4 (induced by source paper "differentiable iterative surface normal estimation"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "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"
    - "pcpnet learning local shape properties from raw point clouds"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

Connected through hyperedge HE-5 (induced by source paper "total denoising unsupervised learning of 3d point cloud cleaning"):
  with members:
    - "noise2self blind denoising by self supervision"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "ec net an edge aware point set consolidation network"
    - "patch based progressive 3d point set upsampling"
    - "pcpnet learning local shape properties from raw point clouds"
    - "a point set generation network for 3d object reconstruction from a single image"

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)