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 "qualitative vision based navigation based on sloped funnel lane concept"):
  Abstract: A new visual navigation based on visual teach and repeat technique is described in this paper. In this kind of navigation, a robot is controlled to follow a path while it is recording a video. Some keyframes are extracted from the video. The extracted keyframes are called visual path and the interval between each two keyframes is called a segment. Later, the robot uses these keyframes to navigate autonomously to follow the desired path. Funnel lane is a recent method to follow visual paths which was proposed by Chen and Birchfield. The method requires a single camera with no calibration or any further calculations such as Jacobian, homography or fundamental matrix. A qualitative comparison between features coordinates is done to follow the visual path. Although experimental results on ground and flying robots show the effectiveness of this method, the method has some limitations. It cannot deal with all types of turning conditions such as rotations in place. Another limitation is an ambiguity between translation and rotation which in some cases may cause the robot to deviate from the desired path. In this paper, we introduce the sloped funnel lane and we explain how it can overcome these limitations. In addition, some challenging scenarios were conducted on a real ground robot to show that. Also, the accuracy and the repeatability of both methods were compared in two different paths. The results show that sloped funnel lane is superior.

Includes members:
  Member "there s no place like home visual teach and repeat for emergency return of multirotor uavs during gps failure":
    also connected through other hyperedges:
      (none)
  Member "uav visual teach and repeat using only semantic object features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tree species identification from bark images using convolutional neural networks")

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)