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: "what makes good synthetic training data for learning disparity and optical flow estimation"
  Abstract: The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations. As a result, the research challenge shifts from devising algorithms to creating suitable and abundant training data for supervised learning. How to efficiently create such training data? The dominant data acquisition method in visual recognition is based on web data and manual annotation. Yet, for many computer vision problems, such as stereo or optical flow estimation, this approach is not feasible because humans cannot manually enter a pixel-accurate flow field. In this paper, we promote the use of synthetically generated data for the purpose of training deep networks on such tasks. We suggest multiple ways to generate such data and evaluate the influence of dataset properties on the performance and generalization properties of the resulting networks. We also demonstrate the benefit of learning schedules that use different types of data at selected stages of the training process.

Connected through hyperedge HE-1 (induced by source paper "scopeflow dynamic scene scoping for optical flow"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "dgc net dense geometric correspondence network"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-2 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
    - "augmented lidar simulator for autonomous driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "efficient road lane marking detection with deep learning"
    - "few shot object detection via feature reweighting"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-3 (induced by source paper "glu net global local universal network for dense flow and correspondences"):
  with members:
    - "geometric image correspondence verification by dense pixel matching"
    - "dynamic context correspondence network for semantic alignment"
    - "an empirical evaluation study on the training of sdc features for dense pixel matching"
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "glampoints greedily learned accurate match points"
    - "semantic attribute matching networks"
    - "lf net learning local features from images"
    - "models matter so does training an empirical study of cnns for optical flow estimation"

Connected through hyperedge HE-4 (induced by source paper "learning to generate synthetic 3d training data through hybrid gradient"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "probabilistic model agnostic meta learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (induced by source paper "region deformer networks for unsupervised depth estimation from unconstrained monocular videos"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "megadepth learning single view depth prediction from internet photos"
    - "digging into self supervised monocular depth estimation"
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

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"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "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 "estimation of absolute scale in monocular slam using synthetic data"):
  with members:
    - "ground plane based absolute scale estimation for monocular visual odometry"
    - "relative geometry aware siamese neural network for 6dof camera relocalization"
    - "deep ordinal regression network for monocular depth estimation"
    - "multimodal unsupervised image to image translation"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "bayesian scale estimation for monocular slam based on generic object detection for correcting scale drift"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "digging into self supervised monocular depth estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep attention based classification network for robust depth prediction"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

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)