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: "semantic scene completion combining colour and depth preliminary experiments"
  Abstract: Semantic scene completion is the task of producing a complete 3D voxel representation of volumetric occupancy with semantic labels for a scene from a single-view observation. We built upon the recent work of Song et al. (CVPR 2017), who proposed SSCnet, a method that performs scene completion and semantic labelling in a single end-to-end 3D convolutional network. SSCnet uses only depth maps as input, even though depth maps are usually obtained from devices that also capture colour information, such as RGBD sensors and stereo cameras. In this work, we investigate the potential of the RGB colour channels to improve SSCnet.

Connected through hyperedge HE-1 (induced by source paper "cascaded context pyramid for full resolution 3d semantic scene completion"):
  with members:
    - "rgbd based dimensional decomposition residual network for 3d semantic scene completion"
    - "efficient semantic scene completion network with spatial group convolution"
    - "agile amulet real time salient object detection with contextual attention"
    - "depth aware cnn for rgb d segmentation"
    - "two stream 3d semantic scene completion"
    - "view volume network for semantic scene completion from a single depth image"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "rgbd based dimensional decomposition residual network for 3d semantic scene completion"):
  with members:
    - "learning pairwise relationship for multi object detection in crowded scenes"
    - "efficient semantic scene completion network with spatial group convolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "two stream 3d semantic scene completion"
    - "view volume network for semantic scene completion from a single depth image"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-3 (induced by source paper "edgenet semantic scene completion from rgb d images"):
  with members:
    - "efficient semantic scene completion network with spatial group convolution"
    - "two stream 3d semantic scene completion"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "view volume network for semantic scene completion from a single depth image"
    - "don t decay the learning rate increase the batch size"
    - "semantic scene completion from a single depth image"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "view volume network for semantic scene completion from a single depth image"):
  with members:
    - "hierarchical surface prediction for 3d object reconstruction"
    - "scancomplete large scale scene completion and semantic segmentation for 3d scans"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "predicting complete 3d models of indoor scenes"
    - "learning rich features from rgb d images for object detection and segmentation"

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)