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: "deepv2d video to depth with differentiable structure from motion"
  Abstract: We propose DeepV2D, an end-to-end differentiable deep learning architecture for predicting depth from a video sequence. We incorporate elements of classical Structure from Motion into an end-to-end trainable pipeline by designing a set of differentiable geometric modules. Our full system alternates between predicting depth and refining camera pose. We estimate depth by building a cost volume over learned features and apply a multi-scale 3D convolutional network for stereo matching. The predicted depth is then sent to the motion module which performs iterative pose updates by mapping optical flow to a camera motion update. We evaluate our proposed system on NYU, KITTI, and SUN3D datasets and show improved results over monocular baselines and deep and classical stereo reconstruction.

Connected through hyperedge HE-1 (induced by source paper "self supervised 3d keypoint learning for ego motion estimation"):
  with members:
    - "two stream networks for self supervised ego motion estimation"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "visual odometry revisited what should be learnt"
    - "neural outlier rejection for self supervised keypoint learning"

Connected through hyperedge HE-2 (induced by source paper "two stream networks for self supervised ego motion estimation"):
  with members:
    - "beyond photometric loss for self supervised ego motion estimation"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "lo net deep real time lidar odometry"
    - "revisiting self supervised visual representation learning"
    - "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"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion"

Connected through hyperedge HE-3 (induced by source paper "video depth estimation by fusing flow to depth proposals"):
  with members:
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "dpsnet end to end deep plane sweep stereo"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "deepmvs learning multi view stereopsis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "mvsnet depth inference for unstructured multi view stereo"

Connected through hyperedge HE-4 (induced by source paper "deepsfm structure from motion via deep bundle adjustment"):
  with members:
    - "dpsnet end to end deep plane sweep stereo"
    - "deep ordinal regression network for monocular depth estimation"
    - "ba net dense bundle adjustment network"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"

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)