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: "deep auxiliary learning for visual localization and odometry"
  Abstract: Localization is an indispensable component of a robot's autonomy stack that enables it to determine where it is in the environment, essentially making it a precursor for any action execution or planning. Although convolutional neural networks have shown promising results for visual localization, they are still grossly outperformed by state-of-the-art local feature-based techniques. In this work, we propose VLocNet, a new convolutional neural network architecture for 6-DoF global pose regression and odometry estimation from consecutive monocular images. Our multitask model incorporates hard parameter sharing, thus being compact and enabling real-time inference, in addition to being end-to-end trainable. We propose a novel loss function that utilizes auxiliary learning to leverage relative pose information during training, thereby constraining the search space to obtain consistent pose estimates. We evaluate our proposed VLocNet on indoor as well as outdoor datasets and show that even our single task model exceeds the performance of state-of-the-art deep architectures for global localization, while achieving competitive performance for visual odometry estimation. Furthermore, we present extensive experimental evaluations utilizing our proposed Geometric Consistency Loss that show the effectiveness of multitask learning and demonstrate that our model is the first deep learning technique to be on par with, and in some cases outperforms state-of-the-art SIFT-based approaches.

Connected through hyperedge HE-1 (induced by source paper "pointloc deep pose regressor for lidar point cloud localization"):
  with members:
    - "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "atloc attention guided camera localization"
    - "deep hough voting for 3d object detection in point clouds"
    - "deeppco end to end point cloud odometry through deep parallel neural network"
    - "flownet3d learning scene flow in 3d point clouds"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-2 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network"):
  with members:
    - "fine grained segmentation networks self supervised segmentation for improved long term visual localization"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "geometric consistency for self supervised end to end visual odometry"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "improved visual relocalization by discovering anchor points"
    - "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"

Connected through hyperedge HE-3 (induced by source paper "da4ad end to end deep attention aware features aided visual localization for autonomous driving"):
  with members:
    - "enforcenet monocular camera localization in large scale indoor sparse lidar point cloud"
    - "sparse to dense hypercolumn matching for long term visual localization"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "vizard reliable visual localization for autonomous vehicles in urban outdoor environments"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "local descriptors optimized for average precision"
    - "from coarse to fine robust hierarchical localization at large scale"

Connected through hyperedge HE-4 (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"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "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-5 (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"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "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-6 (induced by source paper "introduction to camera pose estimation with deep learning"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "learning loss for active learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-7 (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-8 (induced by source paper "understanding the limitations of cnn based absolute camera pose regression"):
  with members:
    - "lf net learning local features from images"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"
    - "improved visual relocalization by discovering anchor points"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "benchmarking 6dof urban visual localization in changing conditions"

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)