human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "self supervised deep pose corrections for robust visual odometry"):
  Abstract: We present a self-supervised deep pose correction (DPC) network that applies pose corrections to a visual odometry estimator to improve its accuracy. Instead of regressing inter-frame pose changes directly, we build on prior work that uses data-driven learning to regress pose corrections that account for systematic errors due to violations of modelling assumptions. Our self-supervised formulation removes any requirement for six-degrees-of-freedom ground truth and, in contrast to expectations, often improves overall navigation accuracy compared to a supervised approach. Through extensive experiments, we show that our self-supervised DPC network can significantly enhance the performance of classical monocular and stereo odometry estimators and substantially out-performs state-of-the-art learning-only approaches.

Includes members:
  Member "does computer vision matter for action":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sim2real transfer learning for 3d pose estimation motion to the rescue")
  Member "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised 3d keypoint learning for ego motion estimation")
  Member "sfmlearner learning monocular depth ego motion using meaningful geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spherical view synthesis for self supervised 360 depth estimation")
  Member "geometric consistency for self supervised end to end visual odometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "every pixel counts joint learning of geometry and motion with 3d holistic understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "a micro lie theory for state estimation in robotics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a code for unscented kalman filtering on manifolds ukf m")

Question: Please tell me which class the source paper 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)