human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
  Abstract: This paper presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under severe viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 10% in accuracy, and 5% average speedup relative to state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "making convolutional networks shift invariant again"
    - "radar only ego motion estimation in difficult settings via graph matching"
    - "omnidirectional cnn for visual place recognition and navigation"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "local descriptor for robust place recognition using lidar intensity"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"

Connected through hyperedge HE-2 (induced by source paper "learning whole image descriptors for real time loop detection andkidnap recovery under large viewpoint difference"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "the limits and potentials of deep learning for robotics"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "neighbourhood consensus networks"
    - "relocalization global optimization and map merging for monocular visual inertial slam"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "lightweight unsupervised deep loop closure"

Connected through hyperedge HE-3 (induced by source paper "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"):
  with members:
    - "condition invariant multi view place recognition"
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "lightweight unsupervised deep loop closure"
    - "deep learning features at scale for visual place recognition"
    - "data efficient decentralized visual slam"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "visual place recognition using landmark distribution descriptors"

Connected through hyperedge HE-4 (induced by source paper "camal context aware multi scale attention framework for lightweight visual place recognition"):
  with members:
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "condition invariant multi view place recognition"
    - "deep learning features at scale for visual place recognition"
    - "multi context attention for human pose estimation"
    - "residual attention network for image classification"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "visual place recognition using landmark distribution descriptors"
    - "cross convolutional layer pooling for image recognition"

Connected through hyperedge HE-5 (induced by source paper "spatio semantic convnet based visual place recognition"):
  with members:
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "deep learning features at scale for visual place recognition"
    - "bags of local convolutional features for scalable instance search"
    - "a taxonomy of deep convolutional neural nets for computer vision"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "convolutional neural network based image representation for visual loop closure detection"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (induced by source paper "are state of the art visual place recognition techniques any good for aerial robotics"):
  with members:
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "memorable maps a framework for re defining places in visual place recognition"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "lightweight unsupervised deep loop closure"
    - "deep learning features at scale for visual place recognition"
    - "data efficient decentralized visual slam"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "spatial transformer networks"

Connected through hyperedge HE-7 (induced by source paper "memorable maps a framework for re defining places in visual place recognition"):
  with members:
    - "deep learning features at scale for visual place recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "convolutional neural network based place recognition"

Question: Please tell me which class the center node 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)