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: "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
  Abstract: We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video - addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric constraints (eg. epipolar geometry) as well as an adaptive photometric loss that supports multiple moving objects, rigid and non-rigid, 2) we extend the model such that it predicts camera intrinsics, making it applicable to uncalibrated video, and 3) we propose several online refinement strategies that rely on the symmetry of our self-supervised loss in training and testing, in particular optimizing model parameters and/or the output of different tasks, thus leveraging their mutual interactions. The idea of jointly optimizing the system output, under all geometric and photometric constraints can be viewed as a dense generalization of classical bundle adjustment. We demonstrate the effectiveness of our method on KITTI and Cityscapes, where we outperform previous self-supervised approaches on multiple tasks. We also show good generalization for transfer learning in YouTube videos.

Connected through hyperedge HE-1 (induced by source paper "low budget unsupervised label query through domain alignment enforcement"):
  with members:
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "confidence regularized self training"
    - "beyond sharing weights for deep domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "consistency based semi supervised active learning towards minimizing labeling budget"
    - "self paced deep learning for weakly supervised object detection"
    - "unsupervised domain adaptation through self supervision"

Connected through hyperedge HE-2 (induced by source paper "occlusion aware unsupervised learning of optical flow from video"):
  with members:
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "digging into self supervised monocular depth estimation"
    - "hidden two stream convolutional networks for action recognition"

Connected through hyperedge HE-3 (induced by source paper "instance wise depth and motion learning from monocular videos"):
  with members:
    - "learning residual flow as dynamic motion from stereo videos"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "learning independent object motion from unlabelled stereoscopic videos"
    - "video instance segmentation"
    - "visuomotor understanding for representation learning of driving scenes"
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "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"

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)