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 "real time scalable dense surfel mapping"):
  Abstract: In this paper, we propose a novel dense surfel mapping system that scales well in different environments with only CPU computation. Using a sparse SLAM system to estimate camera poses, the proposed mapping system can fuse intensity images and depth images into a globally consistent model. The system is carefully designed so that it can build from room-scale environments to urban-scale environments using depth images from RGB-D cameras, stereo cameras or even a monocular camera. First, superpixels extracted from both intensity and depth images are used to model surfels in the system. superpixel-based surfels make our method both run-time efficient and memory efficient. Second, surfels are further organized according to the pose graph of the SLAM system to achieve $O(1)$ fusion time regardless of the scale of reconstructed models. Third, a fast map deformation using the optimized pose graph enables the map to achieve global consistency in real-time. The proposed surfel mapping system is compared with other state-of-the-art methods on synthetic datasets. The performances of urban-scale and room-scale reconstruction are demonstrated using the KITTI dataset and autonomous aggressive flights, respectively. The code is available for the benefit of the community.

Includes members:
  Member "robust dense mapping for large scale dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks")
  Member "vins mono a robust and versatile monocular visual inertial state estimator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "pyramid stereo matching network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "mvdepthnet real time multiview depth estimation neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "depth estimation by learning triangulation and densification of sparse points for multi view stereo")
  Member "orb slam2 an open source slam system for monocular stereo and rgb d cameras":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "voxblox incremental 3d euclidean signed distance fields for on board mav planning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "unsupervised monocular depth estimation with left right consistency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")
  Member "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "occuseg occupancy aware 3d instance segmentation")

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.RO(Robotics)