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 "gmc grid based motion clustering in dynamic environment"):
  Abstract: Conventional SLAM algorithms takes a strong assumption of scene motionlessness, which limits the application in real environments. This paper tries to tackle the challenging visual SLAM issue of moving objects in dynamic environments. We present GMC, grid-based motion clustering approach, a lightweight dynamic object filtering method that is free from high-power and expensive processors. GMC encapsulates motion consistency as the statistical likelihood of detected key points within a certain region. Using this method can we provide real-time and robust correspondence algorithm that can differentiate dynamic objects with static backgrounds. #R##N#We evaluate our system in public TUM dataset. To compare with the state-of-the-art methods, our system can provide more accurate results by detecting dynamic objects.

Includes members:
  Member "stereo vision based semantic 3d object and ego motion tracking for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "visual slam with network uncertainty informed feature selection":
    also connected through other hyperedges:
      (none)
  Member "semantic visual localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "segmap segment based mapping and localization using data driven descriptors")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "segnet a deep convolutional encoder decoder architecture for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)