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: "activestereonet end to end self supervised learning for active stereo systems"
  Abstract: In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of $1/30th$ of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation is edge-preserving and smooths the loss function, which is key to allow the network to reach compelling results. Finally we show how the task of predicting invalid regions, such as occlusions, can be trained end-to-end without ground-truth. This component is crucial to reduce blur and particularly improves predictions along depth discontinuities. Extensive quantitatively and qualitatively evaluations on real and synthetic data demonstrate state of the art results in many challenging scenes.

Connected through hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns"):
  with members:
    - "3dregnet a deep neural network for 3d point registration"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "coherent point drift networks unsupervised learning of non rigid point set registration"
    - "deep sketch based modeling of man made shapes"
    - "3dn 3d deformation network"
    - "deep closest point learning representations for point cloud registration"
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"

Connected through hyperedge HE-2 (induced by source paper "self supervised linear motion deblurring"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "pixel2mesh multi view 3d mesh generation via deformation"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "dpsnet end to end deep plane sweep stereo"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "deepmvs learning multi view stereopsis"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-5 (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-6 (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"
    - "left right comparative recurrent model for stereo matching"
    - "pyramid stereo matching network"
    - "learning monocular depth estimation with unsupervised trinocular assumptions"

Connected through hyperedge HE-7 (induced by source paper "learning unsupervised multi view stereopsis via robust photometric consistency"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "learning a multi view stereo machine"
    - "self supervised learning for stereo matching with self improving ability"
    - "unsupervised learning of depth and ego motion from video"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"

Connected through hyperedge HE-8 (induced by source paper "stereodrnet dilated residual stereo net"):
  with members:
    - "vortex pooling improving context representation in semantic segmentation"
    - "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"
    - "cascade residual learning a two stage convolutional neural 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)