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: "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
  Abstract: Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases. These are good reasons to want instead to capture several smaller sub-scenes that can be joined to make the whole scene. Achieving this has traditionally been difficult: joining sub-scenes that may never have been viewed from the same angle requires a high-quality camera relocaliser that can cope with novel poses, and tracking drift in each sub-scene can prevent them from being joined to make a consistent overall scene. Recent advances, however, have significantly improved our ability to capture medium-sized sub-scenes with little to no tracking drift: real-time globally consistent reconstruction systems can close loops and re-integrate the scene surface on the fly, whilst new visual-inertial odometry approaches can significantly reduce tracking drift during live reconstruction. Moreover, high-quality regression forest-based relocalisers have recently been made more practical by the introduction of a method to allow them to be trained and used online. In this paper, we leverage these advances to present what to our knowledge is the first system to allow multiple users to collaborate interactively to reconstruct dense, voxel-based models of whole buildings using only consumer-grade hardware, a task that has traditionally been both time-consuming and dependent on the availability of specialised hardware. Using our system, an entire house or lab can be reconstructed in under half an hour and at a far lower cost than was previously possible.

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"
    - "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"
    - "exploiting points and lines in regression forests for rgb d camera relocalization"

Connected through hyperedge HE-3 (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"
    - "real time dense stereo matching with elas on fpga accelerated embedded devices"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "infinitam v3 a framework for large scale 3d reconstruction with loop closure"
    - "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-4 (induced by source paper "slamcast large scale real time 3d reconstruction and streaming for immersive multi client live telepresence"):
  with members:
    - "a dynamic hash table for the gpu"
    - "viewport adaptive navigable 360 degree video delivery"
    - "adaptive 360 vr video streaming divide and conquer"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

Connected through hyperedge HE-5 (induced by source paper "the mobile ar sensor logger for android and ios devices"):
  with members:
    - "advio an authentic dataset for visual inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "oxiod the dataset for deep inertial odometry"
    - "active user authentication for smartphones a challenge data set and benchmark results"

Connected through hyperedge HE-6 (induced by source paper "efficient 3d reconstruction and streaming for group scale multi client live telepresence"):
  with members:
    - "slamcast large scale real time 3d reconstruction and streaming for immersive multi client live telepresence"
    - "efficient online surface correction for real time large scale 3d reconstruction"

Connected through hyperedge HE-7 (induced by source paper "optsample a resilient buffer management policy for robotic systems based on optimal message sampling"):
  with members:
    - "scanning the internet for ros a view of security in robotics research"

Connected through hyperedge HE-8 (induced by source paper "collaborative dense slam"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

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)