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: "self supervised learning for stereo matching with self improving ability"
  Abstract: Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations. In this paper, we design a simple convolutional neural network architecture that is able to learn to compute dense disparity maps directly from the stereo inputs. Training is performed in an end-to-end fashion without the need of ground-truth disparity maps. The idea is to use image warping error (instead of disparity-map residuals) as the loss function to drive the learning process, aiming to find a depth-map that minimizes the warping error. While this is a simple concept well-known in stereo matching, to make it work in a deep-learning framework, many non-trivial challenges must be overcome, and in this work we provide effective solutions. Our network is self-adaptive to different unseen imageries as well as to different camera settings. Experiments on KITTI and Middlebury stereo benchmark datasets show that our method outperforms many state-of-the-art stereo matching methods with a margin, and at the same time significantly faster.

Connected through hyperedge HE-1 (induced by source paper "progressive fusion for unsupervised binocular depth estimation using cycled networks"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"

Connected through hyperedge HE-2 (induced by source paper "domain invariant stereo matching networks"):
  with members:
    - "edgestereo an effective multi task learning network for stereo matching and edge detection"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "edgestereo an effective multi task learning network for stereo matching and edge detection"):
  with members:
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "left right comparative recurrent model for stereo matching"
    - "geometry meets semantics for semi supervised monocular depth estimation"
    - "pyramid stereo matching network"
    - "a general and adaptive robust loss function"

Connected through hyperedge HE-4 (induced by source paper "self supervised learning for autonomous vehicles perception a conciliation between analytical and learning methods"):
  with members:
    - "panoptic segmentation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "deep clustering for unsupervised learning of visual features"
    - "dimensionality driven learning with noisy labels"
    - "digging into self supervised monocular depth estimation"
    - "unsupervised learning of depth and ego motion from video"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "mask r cnn"

Connected through hyperedge HE-5 (induced by source paper "mvs 2 deep unsupervised multi view stereo with multi view symmetry"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "open world stereo video matching with deep rnn"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "mvdepthnet real time multiview depth estimation neural network"

Connected through hyperedge HE-6 (induced by source paper "learning unsupervised multi view stereopsis via robust photometric consistency"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deepmvs learning multi view stereopsis"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "learning a multi view stereo machine"
    - "unsupervised learning of depth and ego motion from video"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-7 (induced by source paper "single image deblurring and camera motion estimation with depth map"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "scale recurrent network for deep image deblurring"
    - "learning a discriminative prior for blind image deblurring"
    - "bringing alive blurred moments"
    - "online video deblurring via dynamic temporal blending network"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"

Connected through hyperedge HE-8 (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"
    - "edgestereo an effective multi task learning network for stereo matching and edge detection"
    - "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"

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)