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: "joint layout estimation and global multi view registration for indoor reconstruction"
  Abstract: In this paper, we propose a novel method to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we first build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate between layout estimation and layout-based global registration processes in iterative fashion to complement each other. We extract the scene layout through hierarchical agglomerative clustering and energy-based multi-model fitting in consideration of noisy measurements. Having the estimated scene layout in one hand, we register all the range data through the global iterative closest point algorithm where the positions of 3D points that belong to the layout such as walls and a ceiling are constrained to be close to the layout. We experimentally verify the proposed method with the publicly available synthetic and real-world datasets in both quantitative and qualitative ways.

Connected through hyperedge HE-1 (induced by source paper "flat2layout flat representation for estimating layout of general room types"):
  with members:
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "robot localization in floor plans using a room layout edge extraction network"
    - "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"
    - "panoroom from the sphere to the 3d layout"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-2 (induced by source paper "floorplan jigsaw jointly estimating scene layout and aligning partial scans"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "exploiting 2d floorplan for building scale panorama rgbd alignment"
    - "roomnet end to end room layout estimation"
    - "semantic scene completion from a single depth image"
    - "fine to coarse global registration of rgb d scans"

Connected through hyperedge HE-3 (induced by source paper "monocular object and plane slam in structured environments"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "fusion volumetric object level slam"
    - "structure aware slam using quadrics and planes"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-4 (induced by source paper "floorplan priors for joint camera pose and room layout estimation"):
  with members:
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "planematch patch coplanarity prediction for robust rgb d reconstruction"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "roomnet end to end room layout estimation"
    - "semantic scene completion from a single depth image"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

Connected through hyperedge HE-5 (induced by source paper "planematch patch coplanarity prediction for robust rgb d reconstruction"):
  with members:
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "focal loss for dense object detection"
    - "probabilistic combination of noisy points and planes for rgb d odometry"
    - "fine to coarse global registration of rgb d scans"

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)