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: "fully trainable deep matching"
  Abstract: Deep Matching (DM) is a popular high-quality method for quasi-dense image matching. Despite its name, however, the original DM formulation does not yield a deep neural network that can be trained end-to-end via backpropagation. In this paper, we remove this limitation by rewriting the complete DM algorithm as a convolutional neural network. This results in a novel deep architecture for image matching that involves a number of new layer types and that, similar to recent networks for image segmentation, has a U-topology. We demonstrate the utility of the approach by improving the performance of DM by learning it end-to-end on an image matching task.

Connected through hyperedge HE-1 (induced by source paper "pnp net a hybrid perspective n point network"):
  with members:
    - "learning to find good correspondences"
    - "lift learned invariant feature transform"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-2 (induced by source paper "end to end learnable geometric vision by backpropagating pnp optimization"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "satellite pose estimation with deep landmark regression and nonlinear pose refinement"
    - "expert sample consensus applied to camera re localization"
    - "deep high resolution representation learning for human pose estimation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "numerical coordinate regression with convolutional neural networks"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-3 (induced by source paper "voxelmorph a learning framework for deformable medical image registration"):
  with members:
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "iterative amortized inference"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "inference suboptimality in variational autoencoders"
    - "label driven weakly supervised learning for multimodal deformarle image registration"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "non rigid image registration using fully convolutional networks with deep self supervision"

Connected through hyperedge HE-4 (induced by source paper "bpnp further empowering end to end learning with back propagatable geometric optimization"):
  with members:
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep high resolution representation learning for human pose estimation"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "numerical coordinate regression with convolutional neural networks"
    - "geometric loss functions for camera pose regression with deep learning"
    - "unsupervised learning of depth and ego motion from video"
    - "geometry aware learning of maps for camera localization"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-5 (induced by source paper "semi supervised semantic matching"):
  with members:
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "end to end weakly supervised semantic alignment"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "convolutional neural network architecture for geometric matching"
    - "image based localization using hourglass networks"
    - "unsupervised learning of depth and ego motion from video"
    - "unsupervised deep homography a fast and robust homography estimation model"
    - "fcss fully convolutional self similarity for dense semantic correspondence"

Connected through hyperedge HE-6 (induced by source paper "qatm quality aware template matching for deep learning"):
  with members:
    - "neighbourhood consensus networks"
    - "image to gps verification through a bottom up pattern matching network"
    - "heated up softmax embedding"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "deep matching and validation network an end to end solution to constrained image splicing localization and detection"
    - "learning spread out local feature descriptors"
    - "scnet learning semantic correspondence"

Connected through hyperedge HE-7 (induced by source paper "learning non metric visual similarity for image retrieval"):
  with members:
    - "few example object detection with model communication"
    - "learning deep similarity models with focus ranking for fabric image retrieval"
    - "class weighted convolutional features for visual instance search"
    - "a simple neural network module for relational reasoning"
    - "no fuss distance metric learning using proxies"
    - "learning to compare relation network for few shot learning"
    - "faster r cnn features for instance search"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-8 (induced by source paper "adversarial learning for image forensics deep matching with atrous convolution"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "adversarial learning for semi supervised semantic segmentation"
    - "deep matching and validation network an end to end solution to constrained image splicing localization and detection"
    - "copy move forgery detection based on convolutional kernel network"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep image homography estimation"
    - "semantic segmentation using adversarial networks"
    - "warpnet weakly supervised matching for single view reconstruction"

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)