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: "eigendecomposition free training of deep networks with zero eigenvalue based losses"
  Abstract: Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by finding the eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix representing a linear system. Incorporating this in deep learning frameworks would allow us to explicitly encode known notions of geometry, instead of having the network implicitly learn them from data. However, performing eigendecomposition within a network requires the ability to differentiate this operation. Unfortunately, while theoretically doable, this introduces numerical instability in the optimization process in practice. #R##N#In this paper, we introduce an eigendecomposition-free approach to training a deep network whose loss depends on the eigenvector corresponding to a zero eigenvalue of a matrix predicted by the network. We demonstrate on several tasks, including keypoint matching and 3D pose estimation, that our approach is much more robust than explicit differentiation of the eigendecomposition, It has better convergence properties and yields state-of-the-art results on both tasks.

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 "learning 2d 3d correspondences to solve the blind perspective n point problem"):
  with members:
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "the alignment of the spheres globally optimal spherical mixture alignment for camera pose estimation"
    - "dynamic graph cnn for learning on point clouds"
    - "megadepth learning single view depth prediction from internet photos"
    - "geometric loss functions for camera pose regression with deep learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning to find good correspondences"
    - "globally optimal inlier set maximisation for simultaneous camera pose and feature correspondence"

Connected through hyperedge HE-3 (induced by source paper "to learn or not to learn visual localization from essential matrices"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "deep auxiliary learning for visual localization and odometry"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "efficient 2d 3d matching for multi camera visual localization"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "neighbourhood consensus networks"
    - "from coarse to fine robust hierarchical localization at large scale"

Connected through hyperedge HE-4 (induced by source paper "deephmap combined projection grouping and correspondence learning for full dof pose estimation"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "real time object pose estimation with pose interpreter networks"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "deep 6dpose recovering 6d object pose from a single rgb image"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "a unified framework for multi view multi class object pose estimation"
    - "physics based scene level reasoning for object pose estimation in clutter"
    - "robust 6d object pose estimation in cluttered scenes using semantic segmentation and pose regression networks"

Connected through hyperedge HE-5 (induced by source paper "attentive context normalization for robust permutation equivariant learning"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "lf net learning local features from images"
    - "group normalization"
    - "spectral normalization for generative adversarial networks"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "deep sets"
    - "tangent convolutions for dense prediction in 3d"

Connected through hyperedge HE-6 (induced by source paper "single stage 6d object pose estimation"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "neural guided ransac learning where to sample model hypotheses"
    - "dpod 6d pose object detector and refiner"
    - "backpropagation friendly eigendecomposition"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "lf net learning local features from images"
    - "yolov3 an incremental improvement"
    - "segmentation driven 6d object pose estimation"

Connected through hyperedge HE-7 (induced by source paper "3dregnet a deep neural network for 3d point registration"):
  with members:
    - "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "regnet learning the optimization of direct image to image pose registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "automatic cnn based detection of cardiac mr motion artefacts using k space data augmentation and curriculum learning"
    - "teacher student curriculum learning"

Connected through hyperedge HE-8 (induced by source paper "beyond cartesian representations for local descriptors"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "leveraging outdoor webcams for local descriptor learning"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "from handcrafted to deep local invariant features"
    - "local descriptors optimized for average precision"
    - "geodesc learning local descriptors by integrating geometry constraints"

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)