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: "veram view enhanced recurrent attention model for 3d shape classification"
  Abstract: Multi-view deep neural network is perhaps the most successful approach in 3D shape classification. However, the fusion of multi-view features based on max or average pooling lacks a view selection mechanism, limiting its application in, e.g., multi-view active object recognition by a robot. This paper presents VERAM, a recurrent attention model capable of actively selecting a sequence of views for highly accurate 3D shape classification. VERAM addresses an important issue commonly found in existing attention-based models, i.e., the unbalanced training of the subnetworks corresponding to next view estimation and shape classification. The classification subnetwork is easily overfitted while the view estimation one is usually poorly trained, leading to a suboptimal classification performance. This is surmounted by three essential view-enhancement strategies: 1) enhancing the information flow of gradient backpropagation for the view estimation subnetwork, 2) devising a highly informative reward function for the reinforcement training of view estimation and 3) formulating a novel loss function that explicitly circumvents view duplication. Taking grayscale image as input and AlexNet as CNN architecture, VERAM with 9 views achieves instance-level and class-level accuracy of 95:5% and 95:3% on ModelNet10, 93:7% and 92:1% on ModelNet40, both are the state-of-the-art performance under the same number of views.

Connected through hyperedge HE-1 (induced by source paper "view n gram network for 3d object retrieval"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "learning discriminative 3d shape representations by view discerning networks"
    - "triplet center loss for multi view 3d object retrieval"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "attention is all you need"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"

Connected through hyperedge HE-2 (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"
    - "a deeper look at 3d shape classifiers"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "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-3 (induced by source paper "recurrent 3d attentional networks for end to end active object recognition"):
  with members:
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "look ahead before you leap end to end active recognition by forecasting the effect of motion"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "spatial transformer networks"
    - "end to end training of deep visuomotor policies"
    - "3d shapenets a deep representation for volumetric shapes"

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)