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 "geonet unsupervised learning of dense depth optical flow and camera pose"):
  Abstract: We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos. The three components are coupled by the nature of 3D scene geometry, jointly learned by our framework in an end-to-end manner. Specifically, geometric relationships are extracted over the predictions of individual modules and then combined as an image reconstruction loss, reasoning about static and dynamic scene parts separately. Furthermore, we propose an adaptive geometric consistency loss to increase robustness towards outliers and non-Lambertian regions, which resolves occlusions and texture ambiguities effectively. Experimentation on the KITTI driving dataset reveals that our scheme achieves state-of-the-art results in all of the three tasks, performing better than previously unsupervised methods and comparably with supervised ones.

Includes members:
  Member "geometric loss functions for camera pose regression with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "fast multi frame stereo scene flow with motion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation")
  Member "optical flow in mostly rigid scenes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization")
  Member "topometric localization with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepmel compiling visual multi experience localization into a deep neural network")
  Member "unsupervised learning of depth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "unflow unsupervised learning of optical flow with a bidirectional census loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "demon depth and motion network for learning monocular stereo":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")
  Member "sfm net learning of structure and motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)