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: "3d shape segmentation via shape fully convolutional networks"
  Abstract: We desgin a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation} with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes} which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results.

Connected through hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "object driven text to image synthesis via adversarial training"
    - "cvxnets learnable convex decomposition"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"

Connected through hyperedge HE-2 (induced by source paper "learning to group a bottom up framework for 3d part discovery in unseen categories"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deep part induction from articulated object pairs"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "recurrent pixel embedding for instance grouping"
    - "3d semantic segmentation with submanifold sparse convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"):
  with members:
    - "learning to group and label fine grained shape components"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "point convolutional neural networks by extension operators"
    - "grains generative recursive autoencoders for indoor scenes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "scores shape composition with recursive substructure priors"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-4 (induced by source paper "cosegnet deep co segmentation of 3d shapes with group consistency loss"):
  with members:
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "deep object co segmentation"
    - "faces as lighting probes via unsupervised deep highlight extraction"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "tags2parts discovering semantic regions from shape tags"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-5 (induced by source paper "laplaciannet learning on 3d meshes with laplacian encoding and pooling"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "so net self organizing network for point cloud analysis"
    - "robust watertight manifold surface generation method for shapenet models"
    - "3d mesh segmentation via multi branch 1d convolutional neural networks"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "3d shape segmentation with projective convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "occupancy networks learning 3d reconstruction in function space"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning category specific mesh reconstruction from image collections"
    - "neural processes"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-7 (induced by source paper "foldingnet point cloud auto encoder via deep grid deformation"):
  with members:
    - "deep sets"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "graph based compression of dynamic 3d point cloud sequences"

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)