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: "3dmatch learning the matching of local 3d geometry in range scans"
  Abstract: Establishing correspondences between 3D geometries is essential to a large variety of graphics and vision applications, including 3D reconstruction, localization, and shape matching. Despite significant progress, geometric matching on real-world 3D data is still a challenging task due to the noisy, low-resolution, and incomplete nature of scanning data. These difficulties limit the performance of current state-of-art methods which are typically based on histograms over geometric properties. In this paper, we introduce 3DMatch, a data-driven local feature learner that jointly learns a geometric feature representation and an associated metric function from a large collection of real-world scanning data. We represent 3D geometry using accumulated distance fields around key-point locations. This representation is suited to handle noisy and partial scanning data, and concurrently supports deep learning with convolutional neural networks directly in 3D. To train the networks, we propose a way to automatically generate correspondence labels for deep learning by leveraging existing RGB-D reconstruction algorithms. In our results, we demonstrate that we are able to outperform state-of-the-art approaches by a significant margin. In addition, we show the robustness of our descriptor in a purely geometric sparse bundle adjustment pipeline for 3D reconstruction.

Connected through hyperedge HE-1 (induced by source paper "teaser fast and certifiable point cloud registration"):
  with members:
    - "did you miss the sign a false negative alarm system for traffic sign detectors"
    - "consensus maximization tree search revisited"
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "a practical maximum clique algorithm for matching with pairwise constraints"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "prnet self supervised learning for partial to partial registration"
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"

Connected through hyperedge HE-2 (induced by source paper "densephysnet learning dense physical object representations via multi step dynamic interactions"):
  with members:
    - "environment probing interaction policies"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "interpretable intuitive physics model"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "visual interaction networks"
    - "taking visual motion prediction to new heightfields"

Connected through hyperedge HE-3 (induced by source paper "4d spatio temporal convnets minkowski convolutional neural networks"):
  with members:
    - "modeling 4d fmri data via spatio temporal convolutional neural networks st cnn"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "convolutional neural networks on 3d surfaces using parallel frames"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "tangent convolutions for dense prediction in 3d"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "large scale point cloud semantic segmentation with superpoint graphs"

Connected through hyperedge HE-4 (induced by source paper "surfconv bridging 3d and 2d convolution for rgbd images"):
  with members:
    - "deep sets"
    - "deformable convolutional networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "scale aware face detection"
    - "segcloud semantic segmentation of 3d point clouds"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "sparsity invariant cnns"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-5 (induced by source paper "3d interpreter networks for viewer centered wireframe modeling"):
  with members:
    - "learning a multi view stereo machine"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "marrnet 3d shape reconstruction via 2 5d sketches"
    - "semantic scene completion from a single depth image"
    - "stacked hourglass networks for human pose estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

Connected through hyperedge HE-6 (induced by source paper "scan2mesh from unstructured range scans to 3d meshes"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "deformable shape completion with graph convolutional autoencoders"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-7 (induced by source paper "lpd net 3d point cloud learning for large scale place recognition and environment analysis"):
  with members:
    - "from coarse to fine robust hierarchical localization at large scale"
    - "so net self organizing network for point cloud analysis"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-8 (induced by source paper "pointnetvlad deep point cloud based retrieval for large scale place recognition"):
  with members:
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "3d visual perception for self driving cars using a multi camera system calibration mapping localization and obstacle detection"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "octnet learning deep 3d representations at high resolutions"
    - "tensorflow a system for large scale machine learning"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)