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: "dominant set clustering and pooling for multi view 3d object recognition"
  Abstract: View based strategies for 3D object recognition have proven to be very successful. The state-of-the-art methods now achieve over 90% correct category level recognition performance on appearance images. We improve upon these methods by introducing a view clustering and pooling layer based on dominant sets. The key idea is to pool information from views which are similar and thus belong to the same cluster. The pooled feature vectors are then fed as inputs to the same layer, in a recurrent fashion. This recurrent clustering and pooling module, when inserted in an off-the-shelf pretrained CNN, boosts performance for multi-view 3D object recognition, achieving a new state of the art test set recognition accuracy of 93.8% on the ModelNet 40 database. We also explore a fast approximate learning strategy for our cluster-pooling CNN, which, while sacrificing end-to-end learning, greatly improves its training efficiency with only a slight reduction of recognition accuracy to 93.3%. Our implementation is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"
    - "deep group shuffling random walk for person re identification"

Connected through hyperedge HE-2 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "3d local features for direct pairwise registration"
    - "view n gram network for 3d object retrieval"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "pytorch an imperative style high performance deep learning library"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-3 (induced by source paper "a survey of object classification and detection based on 2d 3d data"):
  with members:
    - "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"
    - "3d a nets 3d deep dense descriptor for volumetric shapes with adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "interpolated convolutional networks for 3d point cloud understanding"):
  with members:
    - "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"
    - "3dcapsule extending the capsule architecture to classify 3d point clouds"
    - "local spectral graph convolution for point set feature learning"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "fully convolutional point networks for large scale point clouds"

Connected through hyperedge HE-5 (induced by source paper "deep multi view learning using neuron wise correlation maximizing regularizers"):
  with members:
    - "multi view intact space learning"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "theory of deep learning iib optimization properties of sgd"
    - "data dependent stability of stochastic gradient descent"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"

Connected through hyperedge HE-6 (induced by source paper "go wider an efficient neural network for point cloud analysis via group convolutions"):
  with members:
    - "gapnet graph attention based point neural network for exploiting local feature of point cloud"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a graph cnn for 3d point cloud classification"
    - "point convolutional neural networks by extension operators"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "local spectral graph convolution for point set feature learning"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-7 (induced by source paper "structured 2d representation of 3d data for shape processing"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "mask r cnn"
    - "3d shape segmentation with projective convolutional networks"
    - "orientation boosted voxel nets for 3d object recognition"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"

Connected through hyperedge HE-8 (induced by source paper "clustering by directly disentangling latent space"):
  with members:
    - "a two step disentanglement method"
    - "y autoencoders disentangling latent representations via sequential encoding"
    - "deep spectral clustering using dual autoencoder network"
    - "from variational to deterministic autoencoders"
    - "adversarial deep embedded clustering on a better trade off between feature randomness and feature drift"
    - "deep clustering for unsupervised learning of visual features"
    - "clustergan latent space clustering in generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"

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)