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 "occlusion robust mvo multimotion estimation through occlusion via motion closure"):
  Abstract: Visual motion estimation is an integral and well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation, which is especially challenging in highly dynamic environments. Such environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. #R##N#Previous work in multiple object tracking focuses on maintaining the integrity of object tracks but usually relies on specific appearance-based descriptors or constrained motion models. These approaches are very effective in specific applications but do not generalize to the full multimotion estimation problem. #R##N#This paper extends the multimotion visual odometry (MVO) pipeline to estimate multiple motions through occlusion, including the camera egomotion, by employing physically founded motion priors. This allows the pipeline to consistently estimate the full trajectory of every motion in a scene and recognize when temporarily occluded motions become unoccluded. The estimation performance of the pipeline is evaluated on real-world data from the Oxford Multimotion Dataset.

Includes members:
  Member "the oxford multimotion dataset multiple se 3 motions with ground truth":
    also connected through other hyperedges:
      (none)
  Member "a white noise on jerk motion prior for continuous time trajectory estimation on se 3":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the oxford multimotion dataset multiple se 3 motions with ground truth")
  Member "multimotion visual odometry mvo simultaneous estimation of camera and third party motions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation")
  Member "geometric multi model fitting with a convex relaxation algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consac robust multi model fitting by conditional sample consensus")

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)