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 siamese learning on stereo image pairs for depth estimation in robotic surgery"
  Abstract: Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery. One popular robotic system is the da Vinci surgical platform, which allows preoperative information to be incorporated into live procedures using Augmented Reality (AR). Scene depth estimation is a prerequisite for AR, as accurate registration requires 3D correspondences between preoperative and intraoperative organ models. In the past decade, there has been much progress on depth estimation for surgical scenes, such as using monocular or binocular laparoscopes [1,2]. More recently, advances in deep learning have enabled depth estimation via Convolutional Neural Networks (CNNs) [3], but training requires a large image dataset with ground truth depths. Inspired by [4], we propose a deep learning framework for surgical scene depth estimation using self-supervision for scalable data acquisition. Our framework consists of an autoencoder for depth prediction, and a differentiable spatial transformer for training the autoencoder on stereo image pairs without ground truth depths. Validation was conducted on stereo videos collected in robotic partial nephrectomy.

Connected through hyperedge HE-1 (induced by source paper "anytime stereo image depth estimation on mobile devices"):
  with members:
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pyramid stereo matching network"
    - "learning affinity via spatial propagation networks"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "blockdrop dynamic inference paths in residual networks"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "demon depth and motion network for learning monocular stereo"
    - "sfm net learning of structure and motion from video"
    - "semi supervised deep learning for monocular depth map prediction"
    - "lift learned invariant feature transform"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "unsupervised monocular depth estimation with left right 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)