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: "cad priors for accurate and flexible instance reconstruction"
  Abstract: We present an efficient and automatic approach for accurate reconstruction of instances of big 3D objects from multiple, unorganized and unstructured point clouds, in presence of dynamic clutter and occlusions. In contrast to conventional scanning, where the background is assumed to be rather static, we aim at handling dynamic clutter where background drastically changes during the object scanning. Currently, it is tedious to solve this with available methods unless the object of interest is first segmented out from the rest of the scene. We address the problem by assuming the availability of a prior CAD model, roughly resembling the object to be reconstructed. This assumption almost always holds in applications such as industrial inspection or reverse engineering. With aid of this prior acting as a proxy, we propose a fully enhanced pipeline, capable of automatically detecting and segmenting the object of interest from scenes and creating a pose graph, online, with linear complexity. This allows initial scan alignment to the CAD model space, which is then refined without the CAD constraint to fully recover a high fidelity 3D reconstruction, accurate up to the sensor noise level. We also contribute a novel object detection method, local implicit shape models (LISM) and give a fast verification scheme. We evaluate our method on multiple datasets, demonstrating the ability to accurately reconstruct objects from small sizes up to $125m^3$.

Connected through hyperedge HE-1 (induced by source paper "learning multiview 3d point cloud registration"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "efficient and robust registration on the 3d special euclidean group"
    - "3d local features for direct pairwise registration"
    - "deep closest point learning representations for point cloud registration"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "latent ransac"
    - "deepmapping unsupervised map estimation from multiple point clouds"

Connected through hyperedge HE-2 (induced by source paper "from planes to corners multi purpose primitive detection in unorganized 3d point clouds"):
  with members:
    - "generic primitive detection in point clouds using novel minimal quadric fits"
    - "planercnn 3d plane detection and reconstruction from a single image"
    - "a minimalist approach to type agnostic detection of quadrics in point clouds"
    - "a large dataset of object scans"

Connected through hyperedge HE-3 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "multi image semantic matching by mining consistent features"
    - "distributable consistent multi object matching"

Connected through hyperedge HE-4 (induced by source paper "3d local features for direct pairwise registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "latent ransac"
    - "bop benchmark for 6d object pose estimation"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "guaranteed outlier removal for point cloud registration with correspondences"

Connected through hyperedge HE-5 (induced by source paper "srinet learning strictly rotation invariant representations for point cloud classification and segmentation"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "p2p net bidirectional point displacement net for shape transform"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "dynamic graph cnn for learning on point clouds"
    - "pointsift a sift like network module for 3d point cloud semantic segmentation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-6 (induced by source paper "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"):
  with members:
    - "dynamic graph convolutional networks"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "scores shape composition with recursive substructure priors"
    - "dynamic graph cnn for learning on point clouds"
    - "pu net point cloud upsampling network"
    - "tangent convolutions for dense prediction in 3d"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "semantic3d net a new large scale point cloud classification benchmark"

Connected through hyperedge HE-7 (induced by source paper "ppfnet global context aware local features for robust 3d point matching"):
  with members:
    - "learning compact geometric features"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric features for voxel based surface recognition"
    - "globally optimal inlier set maximisation for simultaneous camera pose and feature correspondence"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "learning to navigate the energy landscape"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-8 (induced by source paper "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"):
  with members:
    - "convex relaxations for pose graph optimization with outliers"
    - "local optimality and generalization guarantees for the langevin algorithm via empirical metastability"
    - "rotation averaging and strong duality"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "camera pose filtering with local regression geodesics on the riemannian manifold of dual quaternions"
    - "a certifiably correct algorithm for synchronization over the special euclidean group"
    - "spectral synchronization of multiple views in se 3"

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)