human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "on the fly adaptation of regression forests for online camera relocalisation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "infinitam v3 a framework for large scale 3d reconstruction with loop closure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation")
  Member "exploiting 2d floorplan for building scale panorama rgbd alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "is this the right place geometric semantic pose verification for indoor visual localization")
  Member "data efficient decentralized visual slam":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning")
  Member "distributed mapping with privacy and communication constraints lightweight algorithms and object based models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "door slam distributed online and outlier resilient slam for robotic teams")
  Member "semanticpaint interactive 3d labeling and learning at your fingertips":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation")
  Member "a framework for the volumetric integration of depth images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "open world assistive grasping using laser selection")

Question: Please tell me which class the source paper 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)