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 "stereodrnet dilated residual stereo net"):
  Abstract: We propose a system that uses a convolution neural network (CNN) to estimate depth from a stereo pair followed by volumetric fusion of the predicted depth maps to produce a 3D reconstruction of a scene. Our proposed depth refinement architecture, predicts view-consistent disparity and occlusion maps that helps the fusion system to produce geometrically consistent reconstructions. We utilize 3D dilated convolutions in our proposed cost filtering network that yields better filtering while almost halving the computational cost in comparison to state of the art cost filtering architectures.For feature extraction we use the Vortex Pooling architecture. The proposed method achieves state of the art results in KITTI 2012, KITTI 2015 and ETH 3D stereo benchmarks. Finally, we demonstrate that our system is able to produce high fidelity 3D scene reconstructions that outperforms the state of the art stereo system.

Includes members:
  Member "vortex pooling improving context representation in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "practical deep stereo pds toward applications friendly deep stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "segstereo exploiting semantic information for disparity estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "stereonet guided hierarchical refinement for real time edge aware depth prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "edgestereo a context integrated residual pyramid network for stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "activestereonet end to end self supervised learning for active stereo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "pyramid stereo matching network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")

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)