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: "self supervised learning of dense shape correspondence"
  Abstract: We introduce the first completely unsupervised correspondence learning approach for deformable 3D shapes. Key to our model is the understanding that natural deformations (such as changes in pose) approximately preserve the metric structure of the surface, yielding a natural criterion to drive the learning process toward distortion-minimizing predictions. On this basis, we overcome the need for annotated data and replace it by a purely geometric criterion. The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase. In contrast to existing supervised approaches which specialize on the class seen at training time, we demonstrate stronger generalization as well as applicability to a variety of challenging settings. We showcase our method on a wide selection of correspondence benchmarks, where we outperform other methods in terms of accuracy, generalization, and efficiency.

Connected through hyperedge HE-1 (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"
    - "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"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"

Connected through hyperedge HE-2 (induced by source paper "efficient and robust shape correspondence via sparsity enforced quadratic assignment"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "graph matching relax or not"
    - "spectral generalized multi dimensional scaling"
    - "discrete laplace beltrami operator determines discrete riemannian metric"

Connected through hyperedge HE-3 (induced by source paper "fine grained object semantic understanding from correspondences"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "canonical surface mapping via geometric cycle consistency"
    - "spair 71k a large scale benchmark for semantic correspondence"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unsupervised deep learning for structured shape matching"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"

Connected through hyperedge HE-4 (induced by source paper "dynamic graph cnn for learning on point clouds"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "feature denoising for improving adversarial robustness"
    - "point convolutional neural networks by extension operators"
    - "splatnet sparse lattice networks for point cloud processing"
    - "point cloud gan"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "pcpnet learning local shape properties from raw point clouds"
    - "deep functional maps structured prediction for dense shape correspondence"

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)