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 "edgestereo an effective multi task learning network for stereo matching and edge detection"):
  Abstract: Recently, leveraging on the development of end-to-end convolutional neural networks, deep stereo matching networks achieve remarkable performance far exceeding traditional approaches. However, state-of-the-art stereo methods still have difficulties finding correct correspondences in texture-less regions, detailed structures, small objects and near boundaries, which could be alleviated by geometric clues such as edge contours and corresponding constraints. To improve the quality of disparity estimates in these challenging areas, we propose an effective multi-task learning network EdgeStereo composed of a disparity estimation sub-network and an edge detection sub-network, which enables end-to-end predictions of both disparity map and edge map. To effectively incorporates edge cues, we propose the edge-aware smoothness loss and edge feature embedding for inter-task interactions. It is demonstrated that based on our unified model, edge detection task and stereo matching task can promote each other. In addition, we design a compact module called residual pyramid to replace the commonly-used multi-stage cascaded structures or 3-D convolution based regularization modules in current stereo matching networks. By the time of the paper submission, EdgeStereo achieves state-of-the-art performance on the FlyingThings3D dataset, KITTI 2012 and KITTI 2015 stereo benchmarks, outperforming other published stereo matching methods by a noteworthy margin. EdgeStereo also has a better generalization capability for disparity estimation because of the incorporation of edge cues.

Includes members:
  Member "practical deep stereo pds toward applications friendly deep stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "deep stereo matching with explicit cost aggregation sub architecture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised domain adaptation for depth prediction from images")
  Member "edgestereo a context integrated residual pyramid network for stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "left right comparative recurrent model for stereo matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning parallax attention for stereo image super resolution")
  Member "geometry meets semantics for semi supervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "pyramid stereo matching network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "a general and adaptive robust loss function":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "derivative manipulation for general example weighting")

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)