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: "data driven shape analysis and processing"
  Abstract: Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.

Connected through hyperedge HE-1 (induced by source paper "learning part generation and assembly for structure aware shape synthesis"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "structure aware shape synthesis"
    - "learning fuzzy set representations of partial shapes on dual embedding spaces"
    - "learning implicit fields for generative shape modeling"
    - "learning to generate the unseen via part synthesis and composition"

Connected through hyperedge HE-2 (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-3 (induced by source paper "h cnn spatial hashing based cnn for 3d shape analysis"):
  with members:
    - "irregular convolutional neural networks"
    - "deformable convolutional networks"
    - "geometric deep learning going beyond euclidean data"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-4 (induced by source paper "effective rotation invariant point cnn with spherical harmonics kernels"):
  with members:
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "pcn point completion network"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"

Connected through hyperedge HE-5 (induced by source paper "shape2motion joint analysis of motion parts and attributes from 3d shapes"):
  with members:
    - "deep part induction from articulated object pairs"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "3d shape segmentation with projective convolutional networks"
    - "scene flow estimation a survey"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-6 (induced by source paper "semi supervised co analysis of 3d shape styles from projected lines"):
  with members:
    - "multi view convolutional neural networks for 3d shape recognition"
    - "mid level deep pattern mining"
    - "deep residual learning for image recognition"
    - "mid level elements for object detection"
    - "very deep convolutional networks for large scale image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-7 (induced by source paper "depthcut improved depth edge estimation using multiple unreliable channels"):
  with members:
    - "semantic scene completion from a single depth image"
    - "scenenet rgb d 5m photorealistic images of synthetic indoor trajectories with ground truth"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "is the deconvolution layer the same as a convolutional layer"
    - "depth from a single image by harmonizing overcomplete local network predictions"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "stereo matching by training a convolutional neural network to compare image patches"

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.GR(Graphics)