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: "leveraging deep visual descriptors for hierarchical efficient localization"
  Abstract: Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query image and 3D points in the model using local descriptors. However, computational power is often limited on robotic platforms, making this task challenging in large-scale environments. Binary feature descriptors significantly speed up this 2D-3D matching, and have become popular in the robotics community, but also strongly impair the robustness to perceptual aliasing and changes in viewpoint, illumination and scene structure. In this work, we propose to leverage recent advances in deep learning to perform an efficient hierarchical localization. We first localize at the map level using learned image-wide global descriptors, and subsequently estimate a precise pose from 2D-3D matches computed in the candidate places only. This restricts the local search and thus allows to efficiently exploit powerful non-binary descriptors usually dismissed on resource-constrained devices. Our approach results in state-of-the-art localization performance while running in real-time on a popular mobile platform, enabling new prospects for robotics research.

Connected through hyperedge HE-1 (induced by source paper "augmenting visual place recognition with structural cues"):
  with members:
    - "learning 3d segment descriptors for place recognition"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "fine tuning cnn image retrieval with no human annotation"
    - "segmatch segment based place recognition in 3d point clouds"
    - "deep image retrieval learning global representations for image search"
    - "direct sparse odometry"

Connected through hyperedge HE-2 (induced by source paper "localising faster efficient and precise lidar based robot localisation in large scale environments"):
  with members:
    - "segmap segment based mapping and localization using data driven descriptors"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "geometric loss functions for camera pose regression with deep learning"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-3 (induced by source paper "hierarchical multi process fusion for visual place recognition"):
  with members:
    - "multi process fusion visual place recognition using multiple image processing methods"
    - "deep learning features at scale for visual place recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-4 (induced by source paper "to learn or not to learn visual localization from essential matrices"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "efficient 2d 3d matching for multi camera visual localization"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"
    - "from coarse to fine robust hierarchical localization at large scale"

Connected through hyperedge HE-5 (induced by source paper "hierarchical joint scene coordinate classification and regression for visual localization"):
  with members:
    - "fine grained segmentation networks self supervised segmentation for improved long term visual localization"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "expert sample consensus applied to camera re localization"
    - "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"
    - "cascaded parallel filtering for memory efficient image based localization"
    - "lcr net multi person 2d and 3d pose detection in natural images"

Connected through hyperedge HE-6 (induced by source paper "sparse to dense hypercolumn matching for long term visual localization"):
  with members:
    - "lf net learning local features from images"
    - "improving nighttime retrieval based localization"
    - "scene coordinate and correspondence learning for image based localization"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "night to day image translation for retrieval based localization"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-7 (induced by source paper "cascaded parallel filtering for memory efficient image based localization"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "image based localization using lstms for structured feature correlation"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-8 (induced by source paper "segway drive benchmark place recognition and slam data collected by a fleet of delivery robots"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

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)