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 "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation"):
  Abstract: Most SLAM algorithms are based on the assumption that the scene is static. However, in practice, most scenes are dynamic which usually contains moving objects, these methods are not suitable. In this paper, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects. We at first detect and match the interesting points between successive frames by using traditional SLAM methods. Then the interesting points belonging to different motion models (including ego-motion and motion models of rigid moving objects) are segmented by a multi-model fitting approach. Based on the interesting points belonging to the ego-motion, we are able to estimate the trajectory of the camera and reconstruct the static background. The interesting points belonging to the motion models of rigid moving objects are then used to estimate their relative motion models to the camera and reconstruct the 3D models of the objects. We then transform the relative motion to the trajectories of the moving objects in the global reference frame. Finally, we then fuse the 3D models of the moving objects into the 3D map of the environment by considering their motion trajectories to obtain a 4D (3D+time) sequence. DymSLAM obtains information about the dynamic objects instead of ignoring them and is suitable for unknown rigid objects. Hence, the proposed system allows the robot to be employed for high-level tasks, such as obstacle avoidance for dynamic objects. We conducted experiments in a real-world environment where both the camera and the objects were moving in a wide range.

Includes members:
  Member "track to reconstruct and reconstruct to track":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "jrmot a real time 3d multi object tracker and a new large scale dataset")
  Member "stereo based multi motion visual odometry for mobile robots":
    also connected through other hyperedges:
      (none)
  Member "unsupervised monocular depth and ego motion learning with structure and semantics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "learning monocular visual odometry with dense 3d mapping from dense 3d flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sterefo efficient image refocusing with stereo vision")
  Member "stereo vision based semantic 3d object and ego motion tracking for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "ds slam a semantic visual slam towards dynamic environments":
    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 "dynaslam tracking mapping and inpainting in dynamic scenes":
    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 "multimotion visual odometry mvo simultaneous estimation of camera and third party motions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic slam the need for speed")

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)