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: "a deeper look at 3d shape classifiers"
  Abstract: We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial transformations. By varying the number of training examples and employing cross-modal transfer learning we study the role of initialization of existing deep architectures for 3D shape classification. Our analysis shows that multiview methods continue to offer the best generalization even without pretraining on large labeled image datasets, and even when trained on simplified inputs such as binary silhouettes. Furthermore, the performance of voxel-based 3D convolutional networks and point-based architectures can be improved via cross-modal transfer from image representations. Finally, we analyze the robustness of 3D shape classifiers to adversarial transformations and present a novel approach for generating adversarial perturbations of a 3D shape for multiview classifiers using a differentiable renderer. We find that point-based networks are more robust to point position perturbations while voxel-based and multiview networks are easily fooled with the addition of imperceptible noise to the input.

Connected through hyperedge HE-1 (induced by source paper "triangle net towards robustness in point cloud classification"):
  with members:
    - "rotation invariant convolutions for 3d point clouds deep learning"
    - "learning 3d shape completion under weak supervision"
    - "active end effector pose selection for tactile object recognition through monte carlo tree search"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-2 (induced by source paper "equivariant multi view networks"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "spnet deep 3d object classification and retrieval using stereographic projection"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "polar transformer networks"

Connected through hyperedge HE-3 (induced by source paper "hrge net hierarchical relational graph embedding network for multi view 3d shape recognition"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "veram view enhanced recurrent attention model for 3d shape classification"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "relation networks for object detection"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-4 (induced by source paper "3d object recognition with ensemble learning a study of point cloud based deep learning models"):
  with members:
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-5 (induced by source paper "addressing the sim2real gap in robotic 3d object classification"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "3dcapsule extending the capsule architecture to classify 3d point clouds"
    - "tangent convolutions for dense prediction in 3d"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-6 (induced by source paper "is that a chair imagining affordances using simulations of an articulated human body"):
  with members:
    - "kpam keypoint affordances for category level robotic manipulation"
    - "what can i do here leveraging deep 3d saliency and geometry for fast and scalable multiple affordance detection"
    - "learning to act properly predicting and explaining affordances from images"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "what can i do around here deep functional scene understanding for cognitive robots"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-7 (induced by source paper "generating 3d adversarial point clouds"):
  with members:
    - "spatially transformed adversarial examples"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "dynamic graph cnn for learning on point clouds"
    - "generating adversarial examples with adversarial networks"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "robust physical world attacks on deep learning models"

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)