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 "visual global localization with a hybrid wnn cnn approach"):
  Abstract: Currently, self-driving cars rely greatly on the Global Positioning System (GPS) infrastructure, albeit there is an increasing demand for alternative methods for GPS-denied environments. One of them is known as place recognition, which associates images of places with their corresponding positions. We previously proposed systems based on Weightless Neural Networks (WNN) to address this problem as a classification task. This encompasses solely one part of the global localization, which is not precise enough for driverless cars. Instead of just recognizing past places and outputting their poses, it is desired that a global localization system estimates the pose of current place images. In this paper, we propose to tackle this problem as follows. Firstly, given a live image, the place recognition system returns the most similar image and its pose. Then, given live and recollected images, a visual localization system outputs the relative camera pose represented by those images. To estimate the relative camera pose between the recollected and the current images, a Convolutional Neural Network (CNN) is trained with the two images as input and a relative pose vector as output. Together, these systems solve the global localization problem using the topological and metric information to approximate the current vehicle pose. The full approach is compared to a Real- Time Kinematic GPS system and a Simultaneous Localization and Mapping (SLAM) system. Experimental results show that the proposed approach correctly localizes a vehicle 90% of the time with a mean error of 1.20m compared to 1.12m of the SLAM system and 0.37m of the GPS, 89% of the time.

Includes members:
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "towards cnn map compression for camera relocalisation":
    also connected through other hyperedges:
      (none)
  Member "topometric localization with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "relative camera pose estimation using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "planet photo geolocation with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "dsac differentiable ransac for camera localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "convolutional neural network based image representation for visual loop closure detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast loop closure detection via binary content")
  Member "posenet a convolutional network for real time 6 dof camera relocalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)