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 "dedge agmnet a robust multi task learning network for stereo matching and depth edge detection"):
  Abstract: Recently, end-to-end convolutional neural networks have achieved remarkable success in disparity estimation tasks. However, these neural networks usually have difficulty in finding correct correspondences in ill-posed regions, such as texture-less areas, edge details, and small objects. This paper proposes an atrous granular multi-scale network based on depth edge subnetwork(Dedge-AGMNet) to overcome the difficulty above. This work has the following contributions. On one hand, the devised depth edge subnetwork provides the geometric knowledge and depth edge constraints. To incorporate the depth edge cues efficiently, the depth edge-spatial pyramid pooling(Dedge-SPP) module fuses the depth edge features to the disparity estimation branch. And the loss functions are proposed respectively for supervised and unsupervised tasks, which can improve the adaptability of the depth edge auxiliary network. On the other, the designed granular convolution is very suitable for constructing the atrous granular multi-scale (AGM) module. AGM module could capture multi-scale context information that requires fewer parameters and consumes fewer computational resources. In summary,the depth edge cues and multi-scale context information are both beneficial to explore potential corresponding points in ill-posed regions. Integrating the ranks of different stereo datasets, our network outperforms other stereo matching networks and shows very strong robustness for different environments. The Dedge-AGMNet advances state-of-the-art performances on the Sceneflow, KITTI 2012 and KITTI 2015 benchmark datasets.

Includes members:
  Member "res2net a new multi scale backbone architecture":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "edgestereo an effective multi task learning network for stereo matching and edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised monocular depth hints")
  Member "amnet deep atrous multiscale stereo disparity estimation networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tw smnet deep multitask learning of tele wide stereo matching")
  Member "group wise correlation stereo network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "learning depth with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "edgestereo a context integrated residual pyramid network for stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  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)