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: "deep functional maps structured prediction for dense shape correspondence"
  Abstract: We introduce a new framework for learning dense correspondence between deformable 3D shapes. Existing learning based approaches model shape correspondence as a labelling problem, where each point of a query shape receives a label identifying a point on some reference domain; the correspondence is then constructed a posteriori by composing the label predictions of two input shapes. We propose a paradigm shift and design a structured prediction model in the space of functional maps, linear operators that provide a compact representation of the correspondence. We model the learning process via a deep residual network which takes dense descriptor fields defined on two shapes as input, and outputs a soft map between the two given objects. The resulting correspondence is shown to be accurate on several challenging benchmarks comprising multiple categories, synthetic models, real scans with acquisition artifacts, topological noise, and partiality.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"

Connected through hyperedge HE-2 (induced by source paper "on learning sets of symmetric elements"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "deep models for relational databases"
    - "incidence networks for geometric deep learning"
    - "on the limitations of representing functions on sets"
    - "permutation invariant feature restructuring for correlation aware image set based recognition"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "multiview aggregation for learning category specific shape reconstruction"
    - "on the universality of invariant networks"

Connected through hyperedge HE-3 (induced by source paper "the whole is greater than the sum of its nonrigid parts"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "meteornet deep learning on dynamic 3d point cloud sequences"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-4 (induced by source paper "learning diverse fashion collocation by neural graph filtering"):
  with members:
    - "context aware visual compatibility prediction"
    - "learning type aware embeddings for fashion compatibility"
    - "person re identification with deep similarity guided graph neural network"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "focal loss for dense object detection"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-5 (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-6 (induced by source paper "towards accurate vehicle behaviour classification with multi relational graph convolutional networks"):
  with members:
    - "dynamic traffic scene classification with space time coherence"
    - "learning category specific mesh reconstruction from image collections"
    - "attention is all you need"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "mask r cnn"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "learning features by watching objects move"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment"):
  with members:
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "continuous and orientation preserving correspondences via functional maps"
    - "unsupervised deep learning for structured shape matching"
    - "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"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "alternating direction graph matching"

Connected through hyperedge HE-8 (induced by source paper "prnet self supervised learning for partial to partial registration"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "kpconv flexible and deformable convolution for point clouds"
    - "deep part induction from articulated object pairs"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "rethinking imagenet pre training"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"

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)