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: "two stream 3d semantic scene completion"
  Abstract: We propose a new paradigm for advancing 3D semantic scene completion based on RGBD images. We introduce a two stream approach that uses RGB and depth as input channels to a 3D convolutional neural network. Our approach boosts the performance of semantic scene completion by a significant margin. We further provide a study on several input encoding schemes and set a new state of art in 3D semantic scene completion on the NYU dataset.

Connected through hyperedge HE-1 (induced by source paper "3d gated recurrent fusion for semantic scene completion"):
  with members:
    - "rgbd based dimensional decomposition residual network for 3d semantic scene completion"
    - "adaptive fusion for rgb d salient object detection"
    - "efficient semantic scene completion network with spatial group convolution"
    - "scalable place recognition under appearance change for autonomous driving"
    - "unsupervised rgbd video object segmentation using gans"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "scancomplete large scale scene completion and semantic segmentation for 3d scans"

Connected through hyperedge HE-2 (induced by source paper "depth based semantic scene completion with position importance aware loss"):
  with members:
    - "rgbd based dimensional decomposition residual network for 3d semantic scene completion"
    - "efficient semantic scene completion network with spatial group convolution"
    - "locally adaptive learning loss for semantic image segmentation"
    - "yolov3 an incremental improvement"
    - "view volume network for semantic scene completion from a single depth image"
    - "u net for mav based penstock inspection an investigation of focal loss in multi class segmentation for corrosion identification"
    - "adversarial semantic scene completion from a single depth image"
    - "segcloud semantic segmentation of 3d point clouds"

Connected through hyperedge HE-3 (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"
    - "semantic scene completion combining colour and depth preliminary experiments"
    - "agile amulet real time salient object detection with contextual attention"
    - "depth aware cnn for rgb d segmentation"
    - "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-4 (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"
    - "semantic scene completion combining colour and depth preliminary experiments"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "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-5 (induced by source paper "deep reinforcement learning of volume guided progressive view inpainting for 3d point scene completion from a single depth image"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "deep depth completion of a single rgb d image"
    - "free form image inpainting with gated convolution"
    - "view volume network for semantic scene completion from a single depth image"
    - "active object reconstruction using a guided view planner"
    - "adversarial semantic scene completion from a single depth image"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-6 (induced by source paper "edgenet semantic scene completion from rgb d images"):
  with members:
    - "efficient semantic scene completion network with spatial group convolution"
    - "semantic scene completion combining colour and depth preliminary experiments"
    - "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"

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)