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: "3dcnn dqn rnn a deep reinforcement learning framework for semantic parsing of large scale 3d point clouds"
  Abstract: Semantic parsing of large-scale 3D point clouds is an important research topic in computer vision and remote sensing fields. Most existing approaches utilize hand-crafted features for each modality independently and combine them in a heuristic manner. They often fail to consider the consistency and complementary information among features adequately, which makes them difficult to capture high-level semantic structures. The features learned by most of the current deep learning methods can obtain high-quality image classification results. However, these methods are hard to be applied to recognize 3D point clouds due to unorganized distribution and various point density of data. In this paper, we propose a 3DCNN-DQN-RNN method which fuses the 3D convolutional neural network (CNN), Deep Q-Network (DQN) and Residual recurrent neural network (RNN) for an efficient semantic parsing of large-scale 3D point clouds. In our method, an eye window under control of the 3D CNN and DQN can localize and segment the points of the object class efficiently. The 3D CNN and Residual RNN further extract robust and discriminative features of the points in the eye window, and thus greatly enhance the parsing accuracy of large-scale point clouds. Our method provides an automatic process that maps the raw data to the classification results. It also integrates object localization, segmentation and classification into one framework. Experimental results demonstrate that the proposed method outperforms the state-of-the-art point cloud classification methods.

Connected through hyperedge HE-1 (induced by source paper "efficient semantic scene completion network with spatial group convolution"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "inference learning and attention mechanisms that exploit and preserve sparsity in convolutional networks"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "3d semantic segmentation with submanifold sparse convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "pointwise attention based atrous convolutional neural networks"):
  with members:
    - "point attention network for semantic segmentation of 3d point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "deformable filter convolution for point cloud reasoning"
    - "kpconv flexible and deformable convolution for point clouds"
    - "dilated point convolutions on the receptive field of point convolutions"
    - "randla net efficient semantic segmentation of large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-3 (induced by source paper "point cloud processing via recurrent set encoding"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "point convolutional neural networks by extension operators"
    - "splatnet sparse lattice networks for point cloud processing"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-4 (induced by source paper "dar net dynamic aggregation network for semantic scene segmentation"):
  with members:
    - "pointwise an unsupervised point wise feature learning network"
    - "mortonnet self supervised learning of local features in 3d point clouds"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"
    - "tangent convolutions for dense prediction in 3d"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-5 (induced by source paper "an end to end deep rl framework for task arrangement in crowdsourcing platforms"):
  with members:
    - "reinforcement learning to optimize long term user engagement in recommender systems"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep sets"
    - "attention is all you need"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-6 (induced by source paper "ec net an edge aware point set consolidation network"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "dynamic graph cnn for learning on point clouds"
    - "pu net point cloud upsampling network"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"

Connected through hyperedge HE-7 (induced by source paper "3d depthwise convolution reducing model parameters in 3d vision tasks"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "learning a multi view stereo machine"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "segcloud semantic segmentation of 3d point clouds"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"

Connected through hyperedge HE-8 (induced by source paper "outline objects using deep reinforcement learning"):
  with members:
    - "path aggregation network for instance segmentation"
    - "collaborative deep reinforcement learning for joint object search"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deformable convolutional networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"

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)