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: "graph cut ransac"
  Abstract: A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in short, is introduced. To separate inliers and outliers, it runs the graph-cut algorithm in the local optimization (LO) step which is applied when a so-far-the-best model is found. The proposed LO step is conceptually simple, easy to implement, globally optimal and efficient. GC-RANSAC is shown experimentally, both on synthesized tests and real image pairs, to be more geometrically accurate than state-of-the-art methods on a range of problems, e.g. line fitting, homography, affine transformation, fundamental and essential matrix estimation. It runs in real-time for many problems at a speed approximately equal to that of the less accurate alternatives (in milliseconds on standard 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 "consac robust multi model fitting by conditional sample consensus"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "progressive x efficient anytime multi model fitting algorithm"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "multi class model fitting by energy minimization and mode seeking"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"

Connected through hyperedge HE-3 (induced by source paper "a review of point cloud semantic segmentation"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "nonlocal compressive sensing based sar tomography"
    - "associatively segmenting instances and semantics in point clouds"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "vv net voxel vae net with group convolutions for point cloud segmentation"

Connected through hyperedge HE-4 (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"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "gla net an attention network with guided loss for mismatch removal"):
  with members:
    - "nm net mining reliable neighbors for robust feature correspondences"
    - "neural nearest neighbors networks"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "learning to find good correspondences"
    - "training region based object detectors with online hard example mining"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "an evaluation of feature matchers forfundamental matrix estimation"):
  with members:
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "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"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "orb slam a versatile and accurate monocular slam system"
    - "domain size pooling in local descriptors dsp sift"
    - "fundamental matrix estimation a study of error criteria"

Connected through hyperedge HE-7 (induced by source paper "homography from two orientation and scale covariant features"):
  with members:
    - "five point fundamental matrix estimation for uncalibrated cameras"
    - "a clever elimination strategy for efficient minimal solvers"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "radially distorted conjugate translations"
    - "mods fast and robust method for two view matching"

Connected through hyperedge HE-8 (induced by source paper "progressive x efficient anytime multi model fitting algorithm"):
  with members:
    - "searching for representative modes on hypergraphs for robust geometric model fitting"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "multi class model fitting by energy minimization and mode seeking"
    - "mode seeking on hypergraphs for robust geometric model fitting"

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)