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: "geometric consistency for self supervised end to end visual odometry"
  Abstract: With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO) estimation. Most of these proposed solutions rely on supervision, which requires the acquisition of precise ground-truth camera pose information, collected using expensive motion capture systems or high-precision IMU/GPS sensor rigs. In this work, we propose an unsupervised paradigm for deep visual odometry learning. We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels. We leverage geometry as a self-supervisory signal and propose "Composite Transformation Constraints (CTCs)", that automatically generate supervisory signals for training and enforce geometric consistency in the VO estimate. We also present a method of characterizing the uncertainty in VO estimates thus obtained. To evaluate our VO pipeline, we present exhaustive ablation studies that demonstrate the efficacy of end-to-end, self-supervised methodologies to train deep models for monocular VO. We show that leveraging concepts from geometry and incorporating them into the training of a recurrent neural network results in performance competitive to supervised deep VO methods.

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"
    - "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-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"
    - "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"
    - "dpc net deep pose correction for visual localization"

Connected through hyperedge HE-3 (induced by source paper "selectfusion a generic framework to selectively learn multisensory fusion"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "selective sensor fusion for neural visual inertial odometry"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "lo net deep real time lidar odometry"
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "the limits and potentials of deep learning for robotics"

Connected through hyperedge HE-4 (induced by source paper "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "single view stereo matching"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "megadepth learning single view depth prediction from internet photos"
    - "mvsnet depth inference for unstructured multi view stereo"

Connected through hyperedge HE-5 (induced by source paper "shaping belief states with generative environment models for rl"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "preventing posterior collapse with delta vaes"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning attractor dynamics for generative memory"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"

Connected through hyperedge HE-6 (induced by source paper "sequential adversarial learning for self supervised deep visual odometry"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "guided feature selection for deep visual odometry"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "a deeper insight into the undemon unsupervised deep network for depth and ego motion estimation"

Connected through hyperedge HE-7 (induced by source paper "beyond tracking selecting memory and refining poses for deep visual odometry"):
  with members:
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "global pose estimation with an attention based recurrent network"
    - "guided feature selection for deep visual odometry"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"

Connected through hyperedge HE-8 (induced by source paper "vipr visual odometry aided pose regression for 6dof camera localization"):
  with members:
    - "distancenet estimating traveled distance from monocular images using a recurrent convolutional neural network"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "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.RO(Robotics)