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 "video object segmentation based visual servo control and object depth estimation on a mobile robot platform"):
  Abstract: To be useful in everyday environments, robots must be able to identify and locate unstructured, real-world objects. In recent years, video object segmentation has made significant progress on densely separating such objects from background in real and challenging videos. This paper addresses the problem of identifying generic objects and locating them in 3D from a mobile robot platform equipped with an RGB camera. We achieve this by introducing a video object segmentation-based approach to visual servo control and active perception. We validate our approach in experiments using an HSR platform, which subsequently identifies, locates, and grasps objects from the YCB object dataset. We also develop a new Hadamard-Broyden update formulation, which enables HSR to automatically learn the relationship between actuators and visual features without any camera calibration. Using a variety of learned actuator-camera configurations, HSR also tracks people and other dynamic articulated objects.

Includes members:
  Member "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fully convolutional intensive feature flow neural network for text recognition")
  Member "resultant based incremental recovery of camera pose from pairwise matches":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trifocal relative pose from lines at points and its efficient solution")
  Member "self supervised robot in hand object learning":
    also connected through other hyperedges:
      (none)
  Member "densefusion 6d object pose estimation by iterative dense fusion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "fast and accurate online video object segmentation via tracking parts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "efficient video object segmentation via network modulation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adapting object detectors with conditional domain normalization")

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)