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: "complete 3d scene parsing from an rgbd image"
  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 orthogonal walls and the extent of objects, modeled with CAD-like 3D shapes. We parse both the visible and occluded portions of the scene and all observable objects, 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 scenes. 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 spatial consistency. We use support inference to aid interpretation and propose a retrieval scheme that uses convolutional neural networks (CNNs) to classify regions and retrieve objects with similar shapes. We demonstrate the performance of our method on our newly annotated NYUd v2 dataset with detailed 3D shapes.

Connected through hyperedge HE-1 (induced by source paper "3d manhattan room layout reconstruction from a single 360 image"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "roomnet end to end room layout estimation"
    - "matterport3d learning from rgb d data in indoor environments"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-2 (induced by source paper "end to end cad model retrieval and 9dof alignment in 3d scans"):
  with members:
    - "3d local features for direct pairwise registration"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "planercnn 3d plane detection and reconstruction from a single image"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "scan2cad learning cad model alignment in rgb d scans"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "scancomplete large scale scene completion and semantic segmentation for 3d scans"

Connected through hyperedge HE-3 (induced by source paper "holistic 3d scene parsing and reconstruction from a single rgb image"):
  with members:
    - "human centric indoor scene synthesis using stochastic grammar"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "soft nms improving object detection with one line of code"
    - "deformable convolutional networks"
    - "roomnet end to end room layout estimation"
    - "marrnet 3d shape reconstruction via 2 5d sketches"
    - "semantic scene completion from a single depth image"
    - "stacked hourglass networks for human pose estimation"

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)