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 "multi object monocular slam for dynamic environments"):
  Abstract: Multibody monocular SLAM in dynamic environments remains a long-standing challenge in terms of perception and state estimation. Although theoretical solutions exist, practice lags behind, predominantly due to the lack of robust perceptual and predictive models of dynamic participants. The quintessential challenge in Multi-body monocular SLAM in dynamic scenes stems from the problem of unobservability as it is not possible to triangulate a moving object from a moving monocular camera. Under restrictions of object motion the problem can be solved, however even here one is entailed to solve for the single family solution to the relative scale problem. The relative scale problem exists since the dynamic objects that get reconstructed with the monocular camera have a different scale vis a vis the scale space in which the stationary scene is reconstructed. We solve this rather intractable problem by reconstructing dynamic vehicles/participants in single view in metric scale through an object SLAM pipeline. Further, we lift the ego vehicle trajectory obtained from Monocular ORB-SLAM also into metric scales making use of ground plane features thereby resolving the relative scale problem. We present a multi pose-graph optimization formulation to estimate the pose and track dynamic objects in the environment. This optimization helps us reduce the average error in trajectories of multiple bodies in KITTI Tracking sequences. To the best of our knowledge, our method is the first practical monocular multi-body SLAM system to perform dynamic multi-object and ego localization in a unified framework in metric scale.

Includes members:
  Member "cubeslam monocular 3 d object slam":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "stereo vision based semantic 3d object and ego motion tracking for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rodnet object detection under severe conditions using vision radio cross modal supervision")
  Member "orb slam2 an open source slam system for monocular stereo and rgb d cameras":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "in place activated batchnorm for memory optimized training of dnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "yolo9000 better faster stronger":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)