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: "left right comparative recurrent model for stereo matching"
  Abstract: Leveraging the disparity information from both left and right views is crucial for stereo disparity estimation. Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view. However, the conventional left-right consistency check is an isolated post-processing step and heavily hand-crafted. This paper proposes a novel left-right comparative recurrent model to perform left-right consistency checking jointly with disparity estimation. At each recurrent step, the model produces disparity results for both views, and then performs online left-right comparison to identify the mismatched regions which may probably contain erroneously labeled pixels. A soft attention mechanism is introduced, which employs the learned error maps for better guiding the model to selectively focus on refining the unreliable regions at the next recurrent step. In this way, the generated disparity maps are progressively improved by the proposed recurrent model. Extensive evaluations on KITTI 2015, Scene Flow and Middlebury benchmarks validate the effectiveness of our model, demonstrating that state-of-the-art stereo disparity estimation results can be achieved by this new model.

Connected through hyperedge HE-1 (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"
    - "geometry meets semantics for semi supervised monocular depth estimation"
    - "pyramid stereo matching network"
    - "a general and adaptive robust loss function"
    - "self supervised learning for stereo matching with self improving ability"

Connected through hyperedge HE-2 (induced by source paper "learning parallax attention for stereo image super resolution"):
  with members:
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "learning for video super resolution through hr optical flow estimation"
    - "non local recurrent network for image restoration"
    - "multi task learning as multi objective optimization"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-3 (induced by source paper "group wise correlation stereo network"):
  with members:
    - "feature intertwiner for object detection"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "group normalization"
    - "segstereo exploiting semantic information for disparity estimation"
    - "open world stereo video matching with deep rnn"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"

Connected through hyperedge HE-4 (induced by source paper "learning to adapt for stereo"):
  with members:
    - "real time self adaptive deep stereo"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "open world stereo video matching with deep rnn"
    - "learning to reweight examples for robust deep learning"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "pyramid stereo matching network"
    - "learning monocular depth estimation with unsupervised trinocular assumptions"

Connected through hyperedge HE-5 (induced by source paper "monocular depth estimation with directional consistency by deep networks"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "crafting gbd net for object detection"
    - "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "disco depth inference from stereo using context"):
  with members:
    - "segstereo exploiting semantic information for disparity estimation"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "learning for disparity estimation through feature constancy"
    - "deep residual networks with exponential linear unit"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-7 (induced by source paper "real time self adaptive deep stereo"):
  with members:
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "guided stereo matching"
    - "group wise correlation stereo network"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "pyramid stereo matching network"
    - "towards real time unsupervised monocular depth estimation on cpu"

Connected through hyperedge HE-8 (induced by source paper "semi dense stereo matching using dual cnns"):
  with members:
    - "open world stereo video matching with deep rnn"
    - "pyramid stereo matching network"
    - "cbmv a coalesced bidirectional matching volume for disparity estimation"
    - "intel realsense stereoscopic depth cameras"
    - "look wider to match image patches with convolutional neural networks"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"

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)