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: "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
  Abstract: In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due to the recent improvements in local descriptors obtained by learning them from large annotated datasets. Therefore, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and classification. This allows for more realistic, and thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation.

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 "multi view optimization of local feature geometry"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "optimal multi view correction of local affine frames"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "lf net learning local features from images"
    - "neighbourhood consensus networks"
    - "megadepth learning single view depth prediction from internet photos"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "mvsnet depth inference for unstructured multi view stereo"

Connected through hyperedge HE-3 (induced by source paper "solar second order loss and attention for image retrieval"):
  with members:
    - "explicit spatial encoding for deep local descriptors"
    - "learning with average precision training image retrieval with a listwise loss"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "detect to retrieve efficient regional aggregation for image search"
    - "non local neural networks"
    - "attention is all you need"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"

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 "compression of convolutional neural networks for high performance imagematching tasks on mobile devices"):
  with members:
    - "sosnet second order similarity regularization for local descriptor learning"
    - "lf net learning local features from images"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "local descriptors optimized for average precision"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-6 (induced by source paper "geometric image correspondence verification by dense pixel matching"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "neighbourhood consensus networks"
    - "dgc net dense geometric correspondence network"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "image patch matching using convolutional descriptors with euclidean distance"

Connected through hyperedge HE-7 (induced by source paper "self supervised 3d keypoint learning for ego motion estimation"):
  with members:
    - "two stream networks for self supervised ego motion estimation"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "unsuperpoint end to end unsupervised interest point detector and descriptor"
    - "deep high resolution representation learning for human pose estimation"
    - "fcos fully convolutional one stage object detection"
    - "visual odometry revisited what should be learnt"
    - "neural outlier rejection for self supervised keypoint learning"

Connected through hyperedge HE-8 (induced by source paper "superglue learning feature matching with graph neural networks"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "dual graph convolutional network for semantic segmentation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "neural guided ransac learning where to sample model hypotheses"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "deep closest point learning representations for point cloud registration"
    - "r2d2 repeatable and reliable detector and descriptor"
    - "ppfnet global context aware local features for robust 3d point matching"

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)