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: "repeatability is not enough learning affine regions via discriminability"
  Abstract: A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features,that are reliably matched and this necessitates descriptor-based learning. We explore factors that influence such learning and registration: the loss function, descriptor type, geometric parametrization and the trade-off between matchability and geometric accuracy and propose a novel hard negative-constant loss function for learning of affine regions. The affine shape estimator -- AffNet -- trained with the hard negative-constant loss outperforms the state-of-the-art in bag-of-words image retrieval and wide baseline stereo. The proposed training process does not require precisely geometrically aligned patches.The source codes and trained weights are available at this https URL

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 "unifying deep local and global features for image search"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "explicit spatial encoding for deep local descriptors"
    - "local features and visual words emerge in activations"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "learning with average precision training image retrieval with a listwise loss"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "3d local features for direct pairwise registration"
    - "view n gram network for 3d object retrieval"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-4 (induced by source paper "sketchdesc learning local sketch descriptors for multi view correspondence"):
  with members:
    - "sketchzooms deep multi view descriptors for matching line drawings"
    - "hierarchical deep stereo matching on high resolution images"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "from handcrafted to deep local invariant features"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "sketch r2cnn an attentive network for vector sketch recognition"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "fast sketch segmentation and labeling with deep learning"

Connected through hyperedge HE-5 (induced by source paper "contextdesc local descriptor augmentation with cross modality context"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "matchable image retrieval by learning from surface reconstruction"
    - "fix your classifier the marginal value of training the last weight layer"
    - "local descriptors optimized for average precision"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-6 (induced by source paper "r2d2 repeatable and reliable detector and descriptor"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lf net learning local features from images"
    - "from handcrafted to deep local invariant features"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"

Connected through hyperedge HE-7 (induced by source paper "d2 net a trainable cnn for joint detection and description of local features"):
  with members:
    - "lf net learning local features from images"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "megadepth learning single view depth prediction from internet photos"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "superpoint self supervised interest point detection and description"
    - "matching neural paths transfer from recognition to correspondence search"
    - "convolutional neural network architecture for geometric matching"
    - "semantic visual localization"

Connected through hyperedge HE-8 (induced by source paper "df slam a deep learning enhanced visual slam system based on deep local features"):
  with members:
    - "lf net learning local features from images"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "local descriptors optimized for average precision"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "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"
    - "working hard to know your neighbor s margins local descriptor learning loss"

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)