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 "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"):
  Abstract: In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information. The wide range of approaches and the relatively recent growth in interest in the field has meant that a wide range of datasets and assessment methodologies have been proposed, often with a focus only on precision-recall type metrics, making comparison difficult. In this paper we present a comprehensive approach to evaluating the performance of 10 state-of-the-art recently-developed VPR techniques, which utilizes three standardized metrics: (a) Matching Performance b) Matching Time c) Memory Footprint. Together this analysis provides an up-to-date and widely encompassing snapshot of the various strengths and weaknesses of contemporary approaches to the VPR problem. The aim of this work is to help move this particular research field towards a more mature and unified approach to the problem, enabling better comparison and hence more progress to be made in future research.

Includes members:
  Member "condition invariant multi view place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "camal context aware multi scale attention framework for lightweight visual place recognition")
  Member "multi process fusion visual place recognition using multiple image processing methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning")
  Member "lost appearance invariant place recognition for opposite viewpoints using visual semantics":
    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 "lightweight unsupervised deep loop closure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "deep learning features at scale for visual place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "data efficient decentralized visual slam":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning")
  Member "past present and future of simultaneous localization and mapping toward the robust perception age":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.CV(Computer Vision and Pattern Recognition)