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: "toward geometric deep slam"
  Abstract: We present a point tracking system powered by two deep convolutional neural networks. The first network, MagicPoint, operates on single images and extracts salient 2D points. The extracted points are "SLAM-ready" because they are by design isolated and well-distributed throughout the image. We compare this network against classical point detectors and discover a significant performance gap in the presence of image noise. As transformation estimation is more simple when the detected points are geometrically stable, we designed a second network, MagicWarp, which operates on pairs of point images (outputs of MagicPoint), and estimates the homography that relates the inputs. This transformation engine differs from traditional approaches because it does not use local point descriptors, only point locations. Both networks are trained with simple synthetic data, alleviating the requirement of expensive external camera ground truthing and advanced graphics rendering pipelines. The system is fast and lean, easily running 30+ FPS on a single CPU.

Connected through hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice"):
  with members:
    - "self supervised learning of depth and ego motion with differentiable bundle adjustment"
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "understanding the limitations of cnn based absolute camera pose regression"

Connected through hyperedge HE-2 (induced by source paper "under the radar learning to predict robust keypoints for odometry estimation and metric localisation in radar"):
  with members:
    - "masking by moving learning distraction free radar odometry from pose information"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "deep closest point learning representations for point cloud registration"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "superpoint self supervised interest point detection and description"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-3 (induced by source paper "key net keypoint detection by handcrafted and learned cnn filters"):
  with members:
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "lf net learning local features from images"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "repeatability is not enough learning affine regions via discriminability"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "learning to find good correspondences"

Connected through hyperedge HE-4 (induced by source paper "trustable and automated machine learning running with blockchain and its applications"):
  with members:
    - "a unified analytical framework for trustable machine learning and automation running with blockchain"
    - "trustless machine learning contracts evaluating and exchanging machine learning models on the ethereum blockchain"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dynamic routing between capsules"
    - "xgboost a scalable tree boosting system"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "deep fundamental matrix estimation without correspondences"):
  with members:
    - "vision based real estate price estimation"
    - "superpoint self supervised interest point detection and description"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "convolutional neural network architecture for geometric matching"
    - "unsupervised deep homography a fast and robust homography estimation model"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "deep 6 dof tracking"
    - "relative camera pose estimation using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "morse code datasets for machine learning"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "accelerating training of deep neural networks via sparse edge processing"
    - "using synthetic data to train neural networks is model based reasoning"
    - "interleaver design for deep neural networks"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-7 (induced by source paper "matching disparate image pairs using shape aware convnets"):
  with members:
    - "deep spectral correspondence for matching disparate image pairs"
    - "superpoint self supervised interest point detection and description"
    - "unsupervised learning of depth and ego motion from video"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep spectral correspondence for matching disparate image pairs"):
  with members:
    - "superpoint self supervised interest point detection and description"
    - "lift learned invariant feature transform"
    - "wxbs wide baseline stereo generalizations"
    - "learning to compare image patches via convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "two view matching with view synthesis revisited"

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)