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: "visual place recognition using landmark distribution descriptors"
  Abstract: Recent work by Suenderhauf et al. [1] demonstrated improved visual place recognition using proposal regions coupled with features from convolutional neural networks (CNN) to match landmarks between views. In this work we extend the approach by introducing descriptors built from landmark features which also encode the spatial distribution of the landmarks within a view. Matching descriptors then enforces consistency of the relative positions of landmarks between views. This has a significant impact on performance. For example, in experiments on 10 image-pair datasets, each consisting of 200 urban locations with significant differences in viewing positions and conditions, we recorded average precision of around 70% (at 100% recall), compared with 58% obtained using whole image CNN features and 50% for the method in [1].

Connected through hyperedge HE-1 (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-2 (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"
    - "cross convolutional layer pooling for image recognition"

Connected through hyperedge HE-3 (induced by source paper "towards accurate camera geopositioning by image matching"):
  with members:
    - "panorama to panorama matching for location recognition"
    - "large scale image retrieval with attentive deep local features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "wide area image geolocalization with aerial reference imagery"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"):
  with members:
    - "from selective deep convolutional features to compact binary representations for image retrieval"
    - "detect to retrieve efficient regional aggregation for image search"
    - "deep learning features at scale for visual place recognition"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "sift meets cnn a decade survey of instance retrieval"
    - "cross convolutional layer pooling for image recognition"
    - "spatial transformer networks"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-5 (induced by source paper "automated map reading image based localisation in 2 d maps using binary semantic descriptors"):
  with members:
    - "learning from maps visual common sense for autonomous driving"
    - "find your way by observing the sun and other semantic cues"
    - "semantic image based geolocation given a map"
    - "multi track map matching"

Connected through hyperedge HE-6 (induced by source paper "deep learning features at scale for visual place recognition"):
  with members:
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "training a convolutional neural network for appearance invariant place recognition"
    - "convolutional neural network based place recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "neural codes for image retrieval"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-7 (induced by source paper "scale robust localization using general object landmarks"):
  with members:
    - "modelling uncertainty in deep learning for camera relocalization"
    - "deep residual learning for image recognition"
    - "orb slam a versatile and accurate monocular slam system"
    - "on the performance of convnet features for place recognition"
    - "markov localization for mobile robots in dynamic environments"

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)