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: "predicting complete 3d models of indoor scenes"
  Abstract: One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of walls, which must conform to a Manhattan structure but is otherwise flexible, and the layout and extent of objects, modeled with CAD-like 3D shapes. We represent both the visible and occluded portions of the scene, producing a complete 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scene. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and overall consistency. We demonstrate encouraging results on the NYU v2 dataset and highlight a variety of interesting directions for future work.

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"
    - "adversarial semantic scene completion from a single depth image"

Connected through hyperedge HE-2 (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"
    - "3dcnn dqn rnn a deep reinforcement learning framework for semantic parsing of large scale 3d point clouds"
    - "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"

Connected through hyperedge HE-3 (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"
    - "adversarial semantic scene completion from a single depth image"
    - "segan segmenting and generating the invisible"

Connected through hyperedge HE-4 (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"
    - "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"

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"
    - "adversarial semantic scene completion from a single depth image"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "scene recomposition by learning based icp"):
  with members:
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "hindsight experience replay"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "semantic scene completion from a single depth image"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "single image 3d interpreter network"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-8 (induced by source paper "layoutnet reconstructing the 3d room layout from a single rgb image"):
  with members:
    - "roomnet end to end room layout estimation"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "a coarse to fine indoor layout estimation cfile method"
    - "pano2cad room layout from a single panorama image"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic 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)