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: "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
  Abstract: Volumetric models have become a popular representation for 3D scenes in recent years. One breakthrough leading to their popularity was KinectFusion, which focuses on 3D reconstruction using RGB-D sensors. However, monocular SLAM has since also been tackled with very similar approaches. Representing the reconstruction volumetrically as a TSDF leads to most of the simplicity and efficiency that can be achieved with GPU implementations of these systems. However, this representation is memory-intensive and limits applicability to small-scale reconstructions. Several avenues have been explored to overcome this. With the aim of summarizing them and providing for a fast, flexible 3D reconstruction pipeline, we propose a new, unifying framework called InfiniTAM. The idea is that steps like camera tracking, scene representation and integration of new data can easily be replaced and adapted to the user's needs. #R##N#This report describes the technical implementation details of InfiniTAM v3, the third version of our InfiniTAM system. We have added various new features, as well as making numerous enhancements to the low-level code that significantly improve our camera tracking performance. The new features that we expect to be of most interest are (i) a robust camera tracking module; (ii) an implementation of Glocker et al.'s keyframe-based random ferns camera relocaliser; (iii) a novel approach to globally-consistent TSDF-based reconstruction, based on dividing the scene into rigid submaps and optimising the relative poses between them; and (iv) an implementation of Keller et al.'s surfel-based reconstruction approach.

Connected through hyperedge HE-1 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "relative geometry aware siamese neural network for 6dof camera relocalization"
    - "adversarial joint image and pose distribution learning for camera pose regression and refinement"
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-2 (induced by source paper "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "full frame scene coordinate regression for image based localization"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-3 (induced by source paper "flownet3d geometric losses for deep scene flow estimation"):
  with members:
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "deep rigid instance scene flow"
    - "pointflownet learning representations for rigid motion estimation from point clouds"
    - "flownet3d learning scene flow in 3d point clouds"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "motion based object segmentation based on dense rgb d scene flow"
    - "pyramid stereo matching network"

Connected through hyperedge HE-4 (induced by source paper "incremental class discovery for semantic segmentation with rgbd sensing"):
  with members:
    - "fast and accurate semantic mapping through geometric based incremental segmentation"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "segcloud semantic segmentation of 3d point clouds"
    - "pointwise convolutional neural networks"
    - "w net a deep model for fully unsupervised image segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "volumetric instance aware semantic mapping and 3d object discovery"):
  with members:
    - "fast and accurate semantic mapping through geometric based incremental segmentation"
    - "fusion volumetric object level slam"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "incremental object database building 3d models from multiple partial observations"
    - "scenecut joint geometric and object segmentation for indoor scenes"
    - "learning to segment every thing"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-6 (induced by source paper "real time highly accurate dense depth on a power budget using an fpga cpu hybrid soc"):
  with members:
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "real time dense stereo matching with elas on fpga accelerated embedded devices"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "embedded real time stereo estimation via semi global matching on the gpu"
    - "end to end learning of cost volume aggregation for real time dense stereo"

Connected through hyperedge HE-7 (induced by source paper "efficient plane based optimization of geometry and texture for indoor rgb d reconstruction"):
  with members:
    - "plane based optimization of geometry and texture for rgb d reconstruction of indoor scenes"
    - "de noising stabilizing and completing 3d reconstructions on the go using plane priors"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "fine to coarse global registration of rgb d scans"
    - "surface approximation via asymptotic optimal geometric partition"

Connected through hyperedge HE-8 (induced by source paper "real time monocular object model aware sparse slam"):
  with members:
    - "structure aware slam using quadrics and planes"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "yolov3 an incremental improvement"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "dual quadrics from object detection boundingboxes as landmark representations in slam"
    - "pop up slam semantic monocular plane slam for low texture environments"
    - "meaningful maps with object oriented semantic mapping"

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)