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: "learning compact geometric features"
  Abstract: We present an approach to learning features that represent the local geometry around a point in an unstructured point cloud. Such features play a central role in geometric registration, which supports diverse applications in robotics and 3D vision. Current state-of-the-art local features for unstructured point clouds have been manually crafted and none combines the desirable properties of precision, compactness, and robustness. We show that features with these properties can be learned from data, by optimizing deep networks that map high-dimensional histograms into low-dimensional Euclidean spaces. The presented approach yields a family of features, parameterized by dimension, that are both more compact and more accurate than existing descriptors.

Connected through hyperedge HE-1 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "3d local features for direct pairwise registration"
    - "view n gram network for 3d object retrieval"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "continuous geodesic convolutions for learning on 3d shapes"):
  with members:
    - "3d local features for direct pairwise registration"
    - "kpconv flexible and deformable convolution for point clouds"
    - "meshcnn a network with an edge"
    - "quaternion equivariant capsule networks for 3d point clouds"
    - "self supervised learning of dense shape correspondence"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-3 (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-4 (induced by source paper "self supervised point set local descriptors for point cloud registration"):
  with members:
    - "non iterative one step solution for point set registration problem on pose estimation without correspondence"
    - "learning autonomous exploration and mapping with semantic vision"
    - "contextual imagined goals for self supervised robotic learning"
    - "mortonnet self supervised learning of local features in 3d point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "grasp2vec learning object representations from self supervised grasping"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-5 (induced by source paper "d3feat joint learning of dense detection and description of 3d local features"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "kpconv flexible and deformable convolution for point clouds"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "lf net learning local features from images"

Connected through hyperedge HE-6 (induced by source paper "keypointnet a large scale 3d keypoint dataset aggregated from numerous human annotations"):
  with members:
    - "spair 71k a large scale benchmark for semantic correspondence"
    - "relation shape convolutional neural network for point cloud analysis"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-7 (induced by source paper "mgcn descriptor learning using multiscale gcns"):
  with members:
    - "graph wavelet neural network"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "dynamic graph cnn for learning on point clouds"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "geometric deep learning going beyond euclidean data"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "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 "learning rope manipulation policies using dense object descriptors trained on synthetic depth data"):
  with members:
    - "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks"
    - "learning robotic manipulation through visual planning and acting"
    - "grasp2vec learning object representations from self supervised grasping"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "mask r cnn"

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)