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 "anytime stereo image depth estimation on mobile devices"):
  Abstract: Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-of-the-art algorithms force a choice between either generating accurate mappings at a slow pace, or quickly generating inaccurate ones, and additionally these methods typically require far too many parameters to be usable on power- or memory-constrained devices. Motivated by these shortcomings, we propose a novel approach for disparity prediction in the anytime setting. In contrast to prior work, our end-to-end learned approach can trade off computation and accuracy at inference time. Depth estimation is performed in stages, during which the model can be queried at any time to output its current best estimate. Our final model can process 1242$ \times $375 resolution images within a range of 10-35 FPS on an NVIDIA Jetson TX2 module with only marginal increases in error -- using two orders of magnitude fewer parameters than the most competitive baseline. The source code is available at this https URL .

Includes members:
  Member "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction")
  Member "stereonet guided hierarchical refinement for real time edge aware depth prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "liteflownet a lightweight convolutional neural network for optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "softmax splatting for video frame interpolation")
  Member "pyramid stereo matching network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "self supervised siamese learning on stereo image pairs for depth estimation in robotic surgery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction")
  Member "learning affinity via spatial propagation networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arma nets expanding receptive field for dense prediction")
  Member "augmented reality meets computer vision efficient data generation for urban driving scenes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")
  Member "blockdrop dynamic inference paths in residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "switchable precision neural networks")

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)