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: "stereonet guided hierarchical refinement for real time edge aware depth prediction"
  Abstract: This paper presents StereoNet, the first end-to-end deep architecture for real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing high-quality, edge-preserved, quantization-free disparity maps. A key insight of this paper is that the network achieves a sub-pixel matching precision than is a magnitude higher than those of traditional stereo matching approaches. This allows us to achieve real-time performance by using a very low resolution cost volume that encodes all the information needed to achieve high disparity precision. Spatial precision is achieved by employing a learned edge-aware upsampling function. Our model uses a Siamese network to extract features from the left and right image. A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks. Leveraging color input as a guide, this function is capable of producing high-quality edge-aware output. We achieve compelling results on multiple benchmarks, showing how the proposed method offers extreme flexibility at an acceptable computational budget.

Connected through hyperedge HE-1 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems"):
  with members:
    - "hierarchical deep stereo matching on high resolution images"
    - "ga net guided aggregation net for end to end stereo matching"
    - "learned collaborative stereo refinement"
    - "learning depth with convolutional spatial propagation network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "learning energy based inpainting for optical flow"

Connected through hyperedge HE-2 (induced by source paper "learning monocular depth estimation infusing traditional stereo knowledge"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "guided stereo matching"
    - "real time self adaptive deep stereo"
    - "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"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-3 (induced by source paper "ranet ranking attention network for fast video object segmentation"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "egnet edge guidance network for salient object detection"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"

Connected through hyperedge HE-4 (induced by source paper "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "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"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "vision aided absolute trajectory estimation using an unsupervised deep network with online error correction"
    - "megadepth learning single view depth prediction from internet photos"
    - "lego learning edge with geometry all at once by watching videos"

Connected through hyperedge HE-5 (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-6 (induced by source paper "sterefo efficient image refocusing with stereo vision"):
  with members:
    - "real time self adaptive deep stereo"
    - "synthetic depth of field with a single camera mobile phone"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "deeplens shallow depth of field from a single image"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"

Connected through hyperedge HE-7 (induced by source paper "normal assisted stereo depth estimation"):
  with members:
    - "self supervised learning for single view depth and surface normal estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "dpsnet end to end deep plane sweep stereo"
    - "ga net guided aggregation net for end to end stereo matching"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep depth completion of a single rgb d image"

Connected through hyperedge HE-8 (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"
    - "edgestereo a context integrated residual pyramid network for stereo matching"

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)