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 "uwb based system for uav localization in gnss denied environments characterization and dataset"):
  Abstract: Small unmanned aerial vehicles (UAV) have penetrated multiple domains over the past years. In GNSS-denied or indoor environments, aerial robots require a robust and stable localization system, often with external feedback, in order to fly safely. Motion capture systems are typically utilized indoors when accurate localization is needed. However, these systems are expensive and most require a fixed setup. Recently, visual-inertial odometry and similar methods have advanced to a point where autonomous UAVs can rely on them for localization. The main limitation in this case comes from the environment, as well as in long-term autonomy due to accumulating error if loop closure cannot be performed efficiently. For instance, the impact of low visibility due to dust or smoke in post-disaster scenarios might render the odometry methods inapplicable. In this paper, we study and characterize an ultra-wideband (UWB) system for navigation and localization of aerial robots indoors based on Decawave's DWM1001 UWB node. The system is portable, inexpensive and can be battery powered in its totality. We show the viability of this system for autonomous flight of UAVs, and provide open-source methods and data that enable its widespread application even with movable anchor systems. We characterize the accuracy based on the position of the UAV with respect to the anchors, its altitude and speed, and the distribution of the anchors in space. Finally, we analyze the accuracy of the self-calibration of the anchors' positions.

Includes members:
  Member "a general optimization based framework for local odometry estimation with multiple sensors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")
  Member "accurate 3d localization for mav swarms by uwb and imu fusion":
    also connected through other hyperedges:
      (none)
  Member "robust target relative localization with ultra wideband ranging and communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an integrated localization navigation scheme for distance based docking of uavs")
  Member "uwb lidar fusion for cooperative range only slam":
    also connected through other hyperedges:
      (none)
  Member "an integrated localization navigation scheme for distance based docking of uavs":
    also connected through other hyperedges:
      (none)
  Member "vins mono a robust and versatile monocular visual inertial state estimator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep drone racing from simulation to reality with domain randomization")
  Member "ultra wideband aided fast localization and mapping system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph optimization approach to range based localization")
  Member "cooperative relative positioning of mobile users by fusing imu inertial and uwb ranging information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning based pedestrian inertial navigation methods dataset and on device inference")

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)