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: "convolutional neural network based place recognition"
  Abstract: Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.

Connected through hyperedge HE-1 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation"):
  with members:
    - "badgr an autonomous self supervised learning based navigation system"
    - "kidnapped radar topological radar localisation using rotationally invariant metric learning"
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "beyond photometric loss for self supervised ego motion estimation"
    - "towards navigation without precise localization weakly supervised learning of goal directed navigation cost map"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "self supervised learning of depth and motion under photometric inconsistency"

Connected through hyperedge HE-2 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "levelling the playing field a comprehensive comparison of visual place recognition approaches under changing conditions"
    - "learning to follow directions in street view"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "visual place recognition for aerial robotics exploring accuracy computation trade off for local image descriptors"
    - "cross view policy learning for street navigation"
    - "automatic coverage selection for surface based visual localization"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-3 (induced by source paper "a hybrid compact neural architecture for visual place recognition"):
  with members:
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "what can neural networks reason about"
    - "dashnet a hybrid artificial and spiking neural network for high speed object tracking"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "deep learning features at scale for visual place recognition"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-4 (induced by source paper "fast compact and highly scalable visual place recognition through sequence based matching of overloaded representations"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "btel a binary tree encoding approach for visual localization"
    - "link and code fast indexing with graphs and compact regression codes"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "hbst a hamming distance embedding binary search tree for feature based visual place recognition"
    - "learning to index for nearest neighbor search"
    - "billion scale similarity search with gpus"
    - "x view graph based semantic multi view localization"

Connected through hyperedge HE-5 (induced by source paper "improving place recognition using dynamic object detection"):
  with members:
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "orb slam a versatile and accurate monocular slam system"
    - "on the performance of convnet features for place recognition"
    - "imagenet large scale visual recognition challenge"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-6 (induced by source paper "look no deeper recognizing places from opposing viewpoints under varying scene appearance using single view depth estimation"):
  with members:
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "a deeper insight into the undemon unsupervised deep network for depth and ego motion estimation"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "x view graph based semantic multi view localization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-7 (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"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "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"

Connected through hyperedge HE-8 (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"
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "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"

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.CV(Computer Vision and Pattern Recognition)