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: "efficient semantic scene completion network with spatial group convolution"
  Abstract: We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial dimensions rather than feature channel dimension. It divides input voxels into different groups, then conducts 3D sparse convolution on these separated groups. As only valid voxels are considered when performing convolution, computation can be significantly reduced with a slight loss of accuracy. The proposed operations are validated on semantic scene completion task, which aims to predict a complete 3D volume with semantic labels from a single depth image. With SGC, we further present an efficient 3D sparse convolutional network, which harnesses a multiscale architecture and a coarse-to-fine prediction strategy. Evaluations are conducted on the SUNCG dataset, achieving state-of-the-art performance and fast speed. Code is available at this https URL

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"
    - "scalable place recognition under appearance change for autonomous driving"
    - "two stream 3d semantic scene completion"
    - "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"
    - "locally adaptive learning loss for semantic image segmentation"
    - "two stream 3d semantic scene completion"
    - "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 "learning two view correspondences and geometry using order aware network"):
  with members:
    - "contextdesc local descriptor augmentation with cross modality context"
    - "lf net learning local features from images"
    - "hierarchical graph representation learning with differentiable pooling"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "neural nearest neighbors networks"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "self improving visual odometry"
    - "geodesc learning local descriptors by integrating geometry constraints"

Connected through hyperedge HE-4 (induced by source paper "semantickitti a dataset for semantic scene understanding of lidar sequences"):
  with members:
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "yolov3 an incremental improvement"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fully convolutional point networks for large scale point clouds"
    - "know what your neighbors do 3d semantic segmentation of point clouds"

Connected through hyperedge HE-5 (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"
    - "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"
    - "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-6 (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"
    - "semantic scene completion combining colour and depth preliminary experiments"
    - "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-7 (induced by source paper "edgenet semantic scene completion from rgb d images"):
  with members:
    - "semantic scene completion combining colour and depth preliminary experiments"
    - "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-8 (induced by source paper "3d semantic scene completion from a single depth image using adversarial training"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "adversarial semantic scene completion from a single depth image"
    - "semantic scene completion from a single depth image"
    - "learning from simulated and unsupervised images through adversarial training"
    - "semantic segmentation using adversarial networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "improved techniques for training gans"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

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)