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: "fusionnet 3d object classification using multiple data representations"
  Abstract: High-quality 3D object recognition is an important component of many vision and robotics systems. We tackle the object recognition problem using two data representations, to achieve leading results on the Princeton ModelNet challenge. The two representations: 1. Volumetric representation: the 3D object is discretized spatially as binary voxels - $1$ if the voxel is occupied and $0$ otherwise. 2. Pixel representation: the 3D object is represented as a set of projected 2D pixel images. Current leading submissions to the ModelNet Challenge use Convolutional Neural Networks (CNNs) on pixel representations. However, we diverge from this trend and additionally, use Volumetric CNNs to bridge the gap between the efficiency of the above two representations. We combine both representations and exploit them to learn new features, which yield a significantly better classifier than using either of the representations in isolation. To do this, we introduce new Volumetric CNN (V-CNN) architectures.

Connected through hyperedge HE-1 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "octnetfusion learning depth fusion from data"
    - "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning rgb d salient object detection using background enclosure depth contrast and top down features"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-2 (induced by source paper "a survey of object classification and detection based on 2d 3d data"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "so net self organizing network for point cloud analysis"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "an analysis of deep neural network models for practical applications"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-3 (induced by source paper "lp 3dcnn unveiling local phase in 3d convolutional neural networks"):
  with members:
    - "modelling local phase of images and textures with applications in phase denoising and phase retrieval"
    - "spatio temporal channel correlation networks for action classification"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-4 (induced by source paper "discrete rotation equivariance for point cloud recognition"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "intertwiners between induced representations with applications to the theory of equivariant neural networks"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-5 (induced by source paper "mv c3d a spatial correlated multi view 3d convolutional neural networks"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "modeling local geometric structure of 3d point clouds using geo cnn"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "so net self organizing network for point cloud analysis"
    - "angular triplet center loss for multi view 3d shape retrieval"
    - "spnet deep 3d object classification and retrieval using stereographic projection"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-6 (induced by source paper "blended convolution and synthesis for efficient discrimination of 3d shapes"):
  with members:
    - "group normalization"
    - "adversarial training of variational auto encoders for high fidelity image generation"
    - "point convolutional neural networks by extension operators"
    - "local spectral graph convolution for point set feature learning"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "volumetric convolution automatic representation learning in unit ball"

Connected through hyperedge HE-7 (induced by source paper "auto encoding progressive generative adversarial networks for 3d multi object scenes"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "semantic scene completion from a single depth image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning from simulated and unsupervised images through adversarial training"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "pairwise decomposition of image sequences for active multi view recognition"

Connected through hyperedge HE-8 (induced by source paper "cubenet equivariance to 3d rotation and translation"):
  with members:
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "learning steerable filters for rotation equivariant cnns"
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "oriented response networks"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"

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)