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 "simultaneous localization and layout model selection in manhattan worlds"):
  Abstract: In this letter, we will demonstrate how Manhattan structure can be exploited to transform the simultaneous localization and mapping problem, which is typically solved by a nonlinear optimization over feature positions, into a model selection problem solved by a convex optimization over higher-order layout structures, namely walls, floors, and ceilings. Furthermore, we show how our novel formulation leads to an optimization procedure that automatically performs data association and loop closure and which ultimately produces the simplest model of the environment that is consistent with the available measurements. We verify our method on real-world data sets collected with various sensing modalities.

Includes members:
  Member "the open vision computer an integrated sensing and compute system for mobile robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "floornet a unified framework for floorplan reconstruction from 3d scans":
    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 "direction aware semi dense slam":
    also connected through other hyperedges:
      (none)
  Member "robust stereo visual inertial odometry for fast autonomous flight":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "sparse optimization for robust and efficient loop closing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual inertial navigation a concise review")
  Member "orb slam a versatile and accurate monocular slam system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")

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)