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: "improved stereo matching with constant highway networks and reflective confidence learning"
  Abstract: We present an improved three-step pipeline for the stereo matching problem and introduce multiple novelties at each stage. We propose a new highway network architecture for computing the matching cost at each possible disparity, based on multilevel weighted residual shortcuts, trained with a hybrid loss that supports multilevel comparison of image patches. A novel post-processing step is then introduced, which employs a second deep convolutional neural network for pooling global information from multiple disparities. This network outputs both the image disparity map, which replaces the conventional "winner takes all" strategy, and a confidence in the prediction. The confidence score is achieved by training the network with a new technique that we call the reflective loss. Lastly, the learned confidence is employed in order to better detect outliers in the refinement step. The proposed pipeline achieves state of the art accuracy on the largest and most competitive stereo benchmarks, and the learned confidence is shown to outperform all existing alternatives.

Connected through hyperedge HE-1 (induced by source paper "uncertainty estimation for end to end learned dense stereo matching via probabilistic deep learning"):
  with members:
    - "cnn based cost volume analysis as confidence measure for dense matching"
    - "lightweight probabilistic deep networks"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "pyramid stereo matching network"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation for depth prediction from images"):
  with members:
    - "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"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "continuous 3d label stereo matching using local expansion moves"

Connected through hyperedge HE-3 (induced by source paper "tw smnet deep multitask learning of tele wide stereo matching"):
  with members:
    - "amnet deep atrous multiscale stereo disparity estimation networks"
    - "deep ordinal regression network for monocular depth estimation"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "label refinement network for coarse to fine semantic segmentation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "end to end learning of multi scale convolutional neural network for stereo matching"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep stereo matching with explicit cost aggregation sub architecture"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "accurate optical flow via direct cost volume processing"
    - "look wider to match image patches with convolutional neural networks"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-7 (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"
    - "left right comparative recurrent model 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"

Connected through hyperedge HE-8 (induced by source paper "cnn based cost volume analysis as confidence measure for dense matching"):
  with members:
    - "cbmv a coalesced bidirectional matching volume for disparity estimation"
    - "octnet learning deep 3d representations at high resolutions"
    - "fpnn field probing neural networks for 3d data"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)