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: "shapenet convolutional neural networks on non euclidean manifolds"
  Abstract: Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper, we propose ShapeNet, a generalization of the popular convolutional neural networks (CNN) paradigm to non-Euclidean manifolds. Our construction is based on a local geodesic system of polar coordinates to extract "patches", which are then passed through a cascade of filters and linear and non-linear operators. The coefficients of the filters and linear combination weights are optimization variables that are learned to minimize a task-specific cost function. We use ShapeNet to learn invariant shape feature descriptors that significantly outperform recent state-of-the-art methods, and show that previous approaches such as heat and wave kernel signatures, optimal spectral descriptors, and intrinsic shape contexts can be obtained as particular configurations of ShapeNet.

Connected through hyperedge HE-1 (induced by source paper "get rid of suspended animation problem deep diffusive neural network on graph semi supervised classification"):
  with members:
    - "adagcn adaboosting graph convolutional networks into deep models"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep loopy neural network model for graph structured data representation learning"

Connected through hyperedge HE-2 (induced by source paper "learning quadrangulated patches for 3d shape processing"):
  with members:
    - "octnetfusion learning depth fusion from data"
    - "learning quadrangulated patches for 3d shape parameterization and completion"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "octnet learning deep 3d representations at high resolutions"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-3 (induced by source paper "topological based classification using graph convolutional networks"):
  with members:
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "fast interactive object annotation with curve gcn"
    - "regional based query in graph active learning"
    - "pitfalls of graph neural network evaluation"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "semi supervised learning with competitive infection models"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-4 (induced by source paper "topological based classification of paper domains using graph convolutional networks"):
  with members:
    - "semi supervised learning with competitive infection models"
    - "geometric deep learning going beyond euclidean data"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "deep convolutional networks on graph structured data"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "parallel transport convolution a new tool for convolutional neural networks on manifolds"):
  with members:
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "deep convolutional networks on graph structured data"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "variational autoencoders for deforming 3d mesh models"):
  with members:
    - "surfnet generating 3d shape surfaces using deep residual networks"
    - "mesh based autoencoders for localized deformation component analysis"
    - "learning shape abstractions by assembling volumetric primitives"
    - "vconv dae deep volumetric shape learning without object labels"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "unsupervised learning of 3d structure from images"

Connected through hyperedge HE-7 (induced by source paper "learning quadrangulated patches for 3d shape parameterization and completion"):
  with members:
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "shapenet an information rich 3d model repository"
    - "geodesic convolutional neural networks on riemannian manifolds"

Connected through hyperedge HE-8 (induced by source paper "graph based convolutional neural network"):
  with members:
    - "matconvnet convolutional neural networks for matlab"
    - "caffe convolutional architecture for fast feature embedding"
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral networks and locally connected networks on graphs"

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)