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: "dpc net deep pose correction for visual localization"
  Abstract: We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections to the estimator from ground-truth training data. To this end, we derive a novel loss function for learning SE(3) corrections based on a matrix Lie groups approach, with a natural formulation for balancing translation and rotation errors. We use this loss to train a deep pose correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment. Using the KITTI odometry dataset, we demonstrate significant improvements to the accuracy of a computationally-efficient sparse stereo visual odometry pipeline, that render it as accurate as a modern computationally-intensive dense estimator. Further, we show how DPC-Net can be used to mitigate the effect of poorly calibrated lens distortion parameters.

Connected through hyperedge HE-1 (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"
    - "deep auxiliary learning for visual localization and odometry"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "self supervised deep pose corrections for robust visual odometry"):
  with members:
    - "does computer vision matter for action"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "geometric consistency for self supervised end to end visual odometry"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "a micro lie theory for state estimation in robotics"

Connected through hyperedge HE-3 (induced by source paper "probabilistic regression of rotations using quaternion averaging and a deep multi headed network"):
  with members:
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "a micro lie theory for state estimation in robotics"
    - "geometric loss functions for camera pose regression with deep learning"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "relative camera pose estimation using convolutional neural networks"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning matchable colorspace transformations for long term metric visual localization"):
  with members:
    - "night to day image translation for retrieval based localization"
    - "adversarial training for adverse conditions robust metric localisation using appearance transfer"
    - "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"
    - "learning based image enhancement for visual odometry in challenging hdr environments"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "addressing challenging place recognition tasks using generative adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "virtual worlds as proxy for multi object tracking analysis"

Connected through hyperedge HE-5 (induced by source paper "machine vision in the context of robotics a systematic literature review"):
  with members:
    - "adaptive deep learning through visual domain localization"
    - "automatic tool landmark detection for stereo vision in robot assisted retinal surgery"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "calibration of asynchronous camera networks for object reconstruction tasks"):
  with members:
    - "tree morphology for phenotyping from semantics based mapping in orchard environments"
    - "geometric loss functions for camera pose regression with deep learning"
    - "solving the robot world hand eye s calibration problem with iterative methods"
    - "a robotic vision system to measure tree traits"
    - "relative camera pose estimation using convolutional neural networks"
    - "panoptic studio a massively multiview system for social interaction capture"

Connected through hyperedge HE-7 (induced by source paper "calibration of asynchronous camera networks calico"):
  with members:
    - "datasheets for datasets"
    - "geometric loss functions for camera pose regression with deep learning"
    - "solving the robot world hand eye s calibration problem with iterative methods"
    - "relative camera pose estimation using convolutional neural networks"
    - "panoptic studio a massively multiview system for social interaction capture"

Connected through hyperedge HE-8 (induced by source paper "geometric consistency for self supervised end to end visual odometry"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "demon depth and motion network for learning monocular stereo"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "sfm net learning of structure and motion from video"
    - "unsupervised monocular depth estimation with left right consistency"

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)