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: "continuous 3d label stereo matching using local expansion moves"
  Abstract: We present an accurate stereo matching method using  local expansion moves  based on graph cuts. This new move-making scheme is used to efficiently infer per-pixel 3D plane labels on a pairwise Markov random field (MRF) that effectively combines recently proposed slanted patch matching and curvature regularization terms. The local expansion moves are presented as many   $\alpha$      -expansions defined for small grid regions. The local expansion moves extend traditional expansion moves by two ways: localization and spatial propagation. By localization, we use different candidate   $\alpha$      -labels according to the locations of local   $\alpha$      -expansions. By spatial propagation, we design our local   $\alpha$      -expansions to propagate currently assigned labels for nearby regions. With this localization and spatial propagation, our method can efficiently infer MRF models with a continuous label space using randomized search. Our method has several advantages over previous approaches that are based on fusion moves or belief propagation; it produces  submodular moves   deriving a  subproblem optimality ; it helps find good, smooth, piecewise linear disparity maps; it is suitable for parallelization; it can use cost-volume filtering techniques for accelerating the matching cost computations. Even using a simple pairwise MRF, our method is shown to have best performance in the Middlebury stereo benchmark V2 and V3.

Connected through hyperedge HE-1 (induced by source paper "deep depth prior for multi view stereo"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "learned collaborative stereo refinement"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"
    - "deep geometric prior for surface reconstruction"

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"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "u4d unsupervised 4d dynamic scene understanding"):
  with members:
    - "rethinking pose in 3d multi stage refinement and recovery for markerless motion capture"
    - "4d temporally coherent light field video"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "accurate optical flow via direct cost volume processing"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "towards accurate markerless human shape and pose estimation over time"
    - "joint multi person pose estimation and semantic part segmentation"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "hierarchical deep stereo matching on high resolution images"):
  with members:
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "segstereo exploiting semantic information for disparity estimation"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "cbmv a coalesced bidirectional matching volume for disparity estimation"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-5 (induced by source paper "guided stereo matching"):
  with members:
    - "real time self adaptive deep stereo"
    - "fusion of stereo and still monocular depth estimates in a self supervised learning context"
    - "segstereo exploiting semantic information for disparity estimation"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "pyramid stereo matching network"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"

Connected through hyperedge HE-6 (induced by source paper "stereoscopic neural style transfer"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "towards open set identity preserving face synthesis"
    - "learning selfie friendly abstraction from artistic style images"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "characterizing and improving stability in neural style transfer"
    - "revisiting deep intrinsic image decompositions"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "stylebank an explicit representation for neural image style transfer"

Connected through hyperedge HE-7 (induced by source paper "left right comparative recurrent model for stereo matching"):
  with members:
    - "self supervised learning for stereo matching with self improving ability"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "end to end learning of geometry and context for deep stereo regression"
    - "semi supervised deep learning for monocular depth map prediction"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "improved stereo matching with constant highway networks and reflective confidence learning"

Connected through hyperedge HE-8 (induced by source paper "cbmv a coalesced bidirectional matching volume for disparity estimation"):
  with members:
    - "look wider to match image patches with convolutional neural networks"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "end to end training of hybrid cnn crf models for stereo"
    - "computing the stereo matching cost with a convolutional neural network"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "the fast bilateral solver"

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)