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 "probabilistic regression of rotations using quaternion averaging and a deep multi headed network"):
  Abstract: Accurate estimates of rotation are crucial to vision-based motion estimation in augmented reality and robotics. In this work, we present a method to extract probabilistic estimates of rotation from deep regression models. First, we build on prior work and argue that a multi-headed network structure we name HydraNet provides better calibrated uncertainty estimates than methods that rely on stochastic forward passes. Second, we extend HydraNet to targets that belong to the rotation group, SO(3), by regressing unit quaternions and using the tools of rotation averaging and uncertainty injection onto the manifold to produce three-dimensional covariances. Finally, we present results and analysis on a synthetic dataset, learn consistent orientation estimates on the 7-Scenes dataset, and show how we can use our learned covariances to fuse deep estimates of relative orientation with classical stereo visual odometry to improve localization on the KITTI dataset.

Includes members:
  Member "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "self supervised deep pose corrections for robust visual odometry")
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "learning less is more 6d camera localization via 3d surface regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "dpc net deep pose correction for visual localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "vinet visual inertial odometry as a sequence to sequence learning problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "unsupervised learning of depth and ego motion from video":
    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 "relative camera pose estimation using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")

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.CV(Computer Vision and Pattern Recognition)