human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "self supervised monocular depth hints"):
  with members:
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "learning depth with convolutional spatial propagation network"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "single view stereo matching"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-2 (induced by source paper "domain invariant stereo matching networks"):
  with members:
    - "learning to adapt for stereo"
    - "guided stereo matching"
    - "group wise correlation stereo network"
    - "semantic image synthesis with spatially adaptive normalization"
    - "real time self adaptive deep stereo"
    - "segstereo exploiting semantic information for disparity estimation"
    - "feature denoising for improving adversarial robustness"
    - "zoom and learn generalizing deep stereo matching to novel domains"

Connected through hyperedge HE-3 (induced by source paper "depth agmnet an atrous granular multiscale stereo network based on depth edge auxiliary task"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "amnet deep atrous multiscale stereo disparity estimation networks"
    - "group wise correlation stereo network"
    - "learning depth with convolutional spatial propagation network"
    - "segstereo exploiting semantic information for disparity estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "self supervised learning for stereo matching with self improving ability"

Connected through hyperedge HE-4 (induced by source paper "dedge agmnet a robust multi task learning network for stereo matching and depth edge detection"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "amnet deep atrous multiscale stereo disparity estimation networks"
    - "group wise correlation stereo network"
    - "learning depth with convolutional spatial propagation network"
    - "segstereo exploiting semantic information for disparity estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "self supervised learning for stereo matching with self improving ability"

Question: Please tell me which class the center node 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)