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: "full frame scene coordinate regression for image based localization"
  Abstract: Image-based localization, or camera relocalization, is a fundamental problem in computer vision and robotics, and it refers to estimating camera pose from an image. Recent state-of-the-art approaches use learning based methods, such as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress for each pixel in the image its corresponding position in the scene's world coordinate frame, and solve the final pose via a RANSAC-based optimization scheme using the predicted correspondences. In this paper, instead of in a patch-based manner, we propose to perform the scene coordinate regression in a full-frame manner to make the computation efficient at test time and, more importantly, to add more global context to the regression process to improve the robustness. To do so, we adopt a fully convolutional encoder-decoder neural network architecture which accepts a whole image as input and produces scene coordinate predictions for all pixels in the image. However, using more global context is prone to overfitting. To alleviate this issue, we propose to use data augmentation to generate more data for training. In addition to the data augmentation in 2D image space, we also augment the data in 3D space. We evaluate our approach on the publicly available 7-Scenes dataset, and experiments show that it has better scene coordinate predictions and achieves state-of-the-art results in localization with improved robustness on the hardest frames (e.g., frames with repeated structures).

Connected through hyperedge HE-1 (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-2 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "relative geometry aware siamese neural network for 6dof camera relocalization"
    - "adversarial joint image and pose distribution learning for camera pose regression and refinement"
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"

Connected through hyperedge HE-3 (induced by source paper "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "scene coordinate regression with angle based reprojection loss for camera relocalization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "collaborative large scale dense 3d reconstruction with online inter agent pose optimisation"
    - "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"
    - "exploiting points and lines in regression forests for rgb d camera relocalization"

Connected through hyperedge HE-4 (induced by source paper "improving image based localization with deep learning the impact of the loss function"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "image based localization using hourglass networks"
    - "relative camera pose estimation using convolutional neural networks"
    - "spp net deep absolute pose regression with synthetic views"

Connected through hyperedge HE-5 (induced by source paper "decoupling features and coordinates for few shot rgb relocalization"):
  with members:
    - "real time rgb d camera pose estimation in novel scenes using a relocalisation cascade"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "geometric loss functions for camera pose regression with deep learning"
    - "prototypical networks for few shot learning"
    - "learning to learn by gradient descent by gradient descent"
    - "efficient non consecutive feature tracking for robust structure from motion"
    - "matching networks for one shot learning"
    - "dsac differentiable ransac for camera localization"

Connected through hyperedge HE-6 (induced by source paper "scene coordinate regression with angle based reprojection loss for camera relocalization"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "image based localization using hourglass networks"

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)