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: "adversarial semantic scene completion from a single depth image"
  Abstract: We propose a method to reconstruct, complete and semantically label a 3D scene from a single input depth image. We improve the accuracy of the regressed semantic 3D maps by a novel architecture based on adversarial learning. In particular, we suggest using multiple adversarial loss terms that not only enforce realistic outputs with respect to the ground truth, but also an effective embedding of the internal features. This is done by correlating the latent features of the encoder working on partial 2.5D data with the latent features extracted from a variational 3D auto-encoder trained to reconstruct the complete semantic scene. In addition, differently from other approaches that operate entirely through 3D convolutions, at test time we retain the original 2.5D structure of the input during downsampling to improve the effectiveness of the internal representation of our model. We test our approach on the main benchmark datasets for semantic scene completion to qualitatively and quantitatively assess the effectiveness of our proposal.

Connected through hyperedge HE-1 (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"
    - "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"
    - "segcloud semantic segmentation of 3d point clouds"

Connected through hyperedge HE-2 (induced by source paper "object driven multi layer scene decomposition from a single image"):
  with members:
    - "peeking behind objects layered depth prediction from a single image"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep depth completion of a single rgb d image"
    - "planercnn 3d plane detection and reconstruction from a single image"
    - "layer structured 3d scene inference via view synthesis"
    - "stereo magnification learning view synthesis using multiplane images"
    - "segan segmenting and generating the invisible"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "semantickitti a dataset for semantic scene understanding of lidar sequences"):
  with members:
    - "efficient semantic scene completion network with spatial group convolution"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "forknet multi branch volumetric semantic completion from a single depth image"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "view volume network for semantic scene completion from a single depth image"
    - "deep learned full 3d object completion from single view"
    - "patch based image inpainting with generative adversarial networks"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"

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"
    - "two stream 3d semantic scene completion"
    - "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"
    - "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 "3d semantic scene completion from a single depth image using adversarial training"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "efficient semantic scene completion network with spatial group convolution"
    - "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)