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: "volumetric and multi view cnns for object classification on 3d data"
  Abstract: 3D shape models are becoming widely available and easier to capture, making available 3D information crucial for progress in object classification. Current state-of-the-art methods rely on CNNs to address this problem. Recently, we witness two types of CNNs being developed: CNNs based upon volumetric representations versus CNNs based upon multi-view representations. Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations. In this paper, we aim to improve both volumetric CNNs and multi-view CNNs according to extensive analysis of existing approaches. To this end, we introduce two distinct network architectures of volumetric CNNs. In addition, we examine multi-view CNNs, where we introduce multi-resolution filtering in 3D. Overall, we are able to outperform current state-of-the-art methods for both volumetric CNNs and multi-view CNNs. We provide extensive experiments designed to evaluate underlying design choices, thus providing a better understanding of the space of methods available for object classification on 3D data.

Connected through hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "deep sets"
    - "deep imbalanced attribute classification using visual attention aggregation"

Connected through hyperedge HE-2 (induced by source paper "meshcnn a network with an edge"):
  with members:
    - "spherical cnns on unstructured grids"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "convpoint continuous convolutions for point cloud processing"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "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"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-4 (induced by source paper "representation learning on unit ball with 3d roto translational equivariance"):
  with members:
    - "blended convolution and synthesis for efficient discrimination of 3d shapes"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "adversarial training of variational auto encoders for high fidelity image generation"
    - "attention based deep multiple instance learning"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "cubenet equivariance to 3d rotation and translation"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-5 (induced by source paper "fast point r cnn"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "ipod intensive point based object detector for point cloud"
    - "pointpillars fast encoders for object detection from point clouds"
    - "yolo3d end to end real time 3d oriented object bounding box detection from lidar point cloud"
    - "roarnet a robust 3d object detection based on region approximation refinement"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "birdnet a 3d object detection framework from lidar information"

Connected through hyperedge HE-6 (induced by source paper "sdm net deep generative network for structured deformable mesh"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "vv net voxel vae net with group convolutions for point cloud segmentation"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-7 (induced by source paper "rgb d image based object detection from traditional methods to deep learning techniques"):
  with members:
    - "improving image based localization with deep learning the impact of the loss function"
    - "deeply supervised salient object detection with short connections"
    - "geometric loss functions for camera pose regression with deep learning"
    - "rgbd salient object detection via deep fusion"
    - "numerical inversion of srnf maps for elastic shape analysis of genus zero surfaces"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"

Connected through hyperedge HE-8 (induced by source paper "transport based neural style transfer for smoke simulations"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "coupled fluid density and motion from single views"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "neural style transfer a review"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"

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)