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: "zoom and learn generalizing deep stereo matching to novel domains"
  Abstract: Despite the recent success of stereo matching with convolutional neural networks (CNNs), it remains arduous to generalize a pre-trained deep stereo model to a novel domain. A major difficulty is to collect accurate ground-truth disparities for stereo pairs in the target domain. In this work, we propose a self-adaptation approach for CNN training, utilizing both synthetic training data (with ground-truth disparities) and stereo pairs in the new domain (without ground-truths). Our method is driven by two empirical observations. By feeding real stereo pairs of different domains to stereo models pre-trained with synthetic data, we see that: i) a pre-trained model does not generalize well to the new domain, producing artifacts at boundaries and ill-posed regions; however, ii) feeding an up-sampled stereo pair leads to a disparity map with extra details. To avoid i) while exploiting ii), we formulate an iterative optimization problem with graph Laplacian regularization. At each iteration, the CNN adapts itself better to the new domain: we let the CNN learn its own higher-resolution output; at the meanwhile, a graph Laplacian regularization is imposed to discriminatively keep the desired edges while smoothing out the artifacts. We demonstrate the effectiveness of our method in two domains: daily scenes collected by smartphone cameras, and street views captured in a driving car.

Connected through hyperedge HE-1 (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"
    - "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"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

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 "davanet stereo deblurring with view aggregation"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "stereoscopic neural style transfer"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pyramid stereo matching network"
    - "online video deblurring via dynamic temporal blending network"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"

Connected through hyperedge HE-4 (induced by source paper "learning to adapt for stereo"):
  with members:
    - "real time self adaptive deep stereo"
    - "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"
    - "left right comparative recurrent model for stereo matching"
    - "pyramid stereo matching network"
    - "learning monocular depth estimation with unsupervised trinocular assumptions"

Connected through hyperedge HE-5 (induced by source paper "deep surface normal estimation with hierarchical rgb d fusion"):
  with members:
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "depth aware cnn for rgb d segmentation"
    - "deep depth completion of a single rgb d image"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model"
    - "noise2noise learning image restoration without clean data"
    - "deep graph laplacian regularization for robust denoising of real images"
    - "deformable shape completion with graph convolutional autoencoders"

Connected through hyperedge HE-6 (induced by source paper "learning dense wide baseline stereo matching for people"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "stereodrnet dilated residual stereo net"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "feature mapping for learning fast and accurate 3d pose inference from synthetic images"

Connected through hyperedge HE-7 (induced by source paper "deep end to end alignment and refinement for time of flight rgb d module"):
  with members:
    - "deep rigid instance scene flow"
    - "single view stereo matching"
    - "tackling 3d tof artifacts through learning and the flat dataset"
    - "dsr direct self rectification for uncalibrated dual lens cameras"
    - "deeptof off the shelf real time correction of multipath interference in time of flight imaging"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-8 (induced by source paper "depth estimation in nighttime using stereo consistent cyclic translations"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "night to day image translation for retrieval based localization"
    - "pyramid stereo matching network"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "learning from simulated and unsupervised images through adversarial training"

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)