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 "hybrid feature based slam prototype"):
  Abstract: The development of data innovation as of late and the expanded limit, has permitted the acquaintance of artificial vision connected with SLAM, offering ascend to what is known as Visual SLAM. The objective of this paper is to build up a route framework dependent on Visual SLAM to get a robot to a fundamental and new condition, have the capacity to set and make a three-dimensional guide thereof, utilizing just as sources of info recording your way with a stereo vision camera. The consequence of this analysis is that the framework Visual SLAM together with the combination of Fast SLAM (combination of kalman with particulate filter and SIFT) perceive and recognize characteristic points in images so adequately exact and unambiguous. This framework uses MATLAB, since its adaptability and comfort for performing a wide range of tests. The program has been tested by inserting a prerecorded video input with a camera stereo in which a course is done by an office environment. The algorithm initially locates points of interest in a stereo frame captured by the camera. These will be located in 3D and they associate an identification descriptor. In the next frame, the camera likewise identified points of interest and it will be compared which of them have been previously detected by comparing their descriptors. This process is known as "data association" and its successful completion is fundamental to the SLAM algorithm. The position data of the robot and points interest stored in data structures known as "particles" that evolve independently. Its management is very important for the proper functioning of the algorithm Fast SLAM. The results are found to be satisfactory.

Includes members:
  Member "unsupervised odometry and depth learning for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modelhub ai dissemination platform for deep learning models")
  Member "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "endol2h deep super resolution for capsule endoscopy")
  Member "endosensorfusion particle filtering based multi sensory data fusion with switching state space model for endoscopic capsule robots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of deep learning techniques for mobile robot applications")

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.CV(Computer Vision and Pattern Recognition)