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: "mods fast and robust method for two view matching"
  Abstract: Abstract   A novel algorithm for wide-baseline matching called MODS—matching on demand with view synthesis—is presented. The MODS algorithm is experimentally shown to solve a broader range of wide-baseline problems than the state of the art while being nearly as fast as standard matchers on simple problems. The apparent robustness vs. speed trade-off is finessed by the use of progressively more time-consuming feature detectors and by on-demand generation of synthesized images that is performed until a reliable estimate of geometry is obtained.  We introduce an improved method for tentative correspondence selection, applicable both with and without view synthesis. A modification of the standard first to second nearest distance rule increases the number of correct matches by 5–20% at no additional computational cost.  Performance of the MODS algorithm is evaluated on several standard publicly available datasets, and on a new set of geometrically challenging wide baseline problems that is made public together with the ground truth. Experiments show that the MODS outperforms the state-of-the-art in robustness and speed. Moreover, MODS performs well on other classes of difficult two-view problems like matching of images from different modalities, with wide temporal baseline or with significant lighting changes.

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 "from handcrafted to deep local features"):
  with members:
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "quad networks unsupervised learning to rank for interest point detection"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-3 (induced by source paper "leveraging outdoor webcams for local descriptor learning"):
  with members:
    - "matchable image retrieval by learning from surface reconstruction"
    - "from handcrafted to deep local invariant features"
    - "local descriptors optimized for average precision"
    - "learning a local feature descriptor for 3d lidar scans"
    - "structure from motion using dense cnn features with keypoint relocalization"
    - "a large dataset for improving patch matching"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-4 (induced by source paper "minimal solutions for relative pose with a single affine correspondence"):
  with members:
    - "homography from two orientation and scale covariant features"
    - "plmp point line minimal problems in complete multi view visibility"
    - "five point fundamental matrix estimation for uncalibrated cameras"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "on the existence of epipolar matrices"

Connected through hyperedge HE-5 (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"
    - "graph cut ransac"

Connected through hyperedge HE-6 (induced by source paper "feature based groupwise registration of historical aerial images to present day ortho photo maps"):
  with members:
    - "image stitching by line guided local warping with global similarity constraint"
    - "aerial image geolocalization from recognition and matching of roads and intersections"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

Connected through hyperedge HE-7 (induced by source paper "optimal multi view correction of local affine frames"):
  with members:
    - "multi class model fitting by energy minimization and mode seeking"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "radially distorted conjugate translations"

Connected through hyperedge HE-8 (induced by source paper "relative planar motion for vehicle mounted cameras from a single affine correspondence"):
  with members:
    - "repeatability is not enough learning affine regions via discriminability"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "graph cut ransac"

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)