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: "gvnn neural network library for geometric computer vision"
  Abstract: We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are often used as parametric transformations on the data in geometric computer vision. These layers can be inserted within a neural network much in the spirit of the original spatial transformers and allow backpropagation to enable end-to-end learning of a network involving any domain knowledge in geometric computer vision. This opens up applications in learning invariance to 3D geometric transformation for place recognition, end-to-end visual odometry, depth estimation and unsupervised learning through warping with a parametric transformation for image reconstruction error.

Connected through hyperedge HE-1 (induced by source paper "futuremapping the computational structure of spatial ai systems"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "algorithmic performance accuracy trade off in 3d vision applications using hypermapper"
    - "da rnn semantic mapping with data associated recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of depth and ego motion from video"):
  with members:
    - "end to end learning of geometry and context for deep stereo regression"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "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"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning in remote sensing a review"):
  with members:
    - "toward seamless multiview scene analysis from satellite to street level"
    - "look wider to match image patches with convolutional neural networks"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "lift learned invariant feature transform"
    - "a survey on object detection in optical remote sensing images"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-4 (induced by source paper "following gaze across views"):
  with members:
    - "deep image homography estimation"
    - "unsupervised learning of 3d structure from images"
    - "spatial transformer networks"
    - "movieqa understanding stories in movies through question answering"
    - "faster r cnn towards real time object detection with region proposal 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)