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 "eng end to end neural geometry for robust depth and pose estimation using cnns"):
  Abstract: Recovering structure and motion parameters given a image pair or a sequence of images is a well studied problem in computer vision. This is often achieved by employing Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) algorithms based on the real-time requirements. Recently, with the advent of Convolutional Neural Networks (CNNs) researchers have explored the possibility of using machine learning techniques to reconstruct the 3D structure of a scene and jointly predict the camera pose. In this work, we present a framework that achieves state-of-the-art performance on single image depth prediction for both indoor and outdoor scenes. The depth prediction system is then extended to predict optical flow and ultimately the camera pose and trained end-to-end. Our motion estimation framework outperforms the previous motion prediction systems and we also demonstrate that the state-of-the-art metric depths can be further improved using the knowledge of pose.

Includes members:
  Member "orb slam2 an open source slam system for monocular stereo and rgb d cameras":
    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 "unsupervised learning of depth and ego motion from video":
    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 "joint prediction of depths normals and surface curvature from rgb images using cnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "360 o surface regression with a hyper sphere loss")
  Member "pwc net cnns for optical flow using pyramid warping and cost volume":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "probflow joint optical flow and uncertainty estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation")
  Member "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "demon depth and motion network for learning monocular stereo":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")

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)