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: "local descriptors optimized for average precision"
  Abstract: Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks. Learning-based approaches improve performance in certain tasks, but still cannot replace handcrafted features in general. In this paper, we improve the learning of local feature descriptors by optimizing the performance of descriptor matching, which is a common stage that follows descriptor extraction in local feature based pipelines, and can be formulated as nearest neighbor retrieval. Specifically, we directly optimize a ranking-based retrieval performance metric, Average Precision, using deep neural networks. This general-purpose solution can also be viewed as a listwise learning to rank approach, which is advantageous compared to recent local ranking approaches. On standard benchmarks, descriptors learned with our formulation achieve state-of-the-art results in patch verification, patch retrieval, and image matching.

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 "da4ad end to end deep attention aware features aided visual localization for autonomous driving"):
  with members:
    - "enforcenet monocular camera localization in large scale indoor sparse lidar point cloud"
    - "sparse to dense hypercolumn matching for long term visual localization"
    - "understanding the limitations of cnn based absolute camera pose regression"
    - "vizard reliable visual localization for autonomous vehicles in urban outdoor environments"
    - "deep auxiliary learning for visual localization and odometry"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "from coarse to fine robust hierarchical localization at large scale"

Connected through hyperedge HE-4 (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"
    - "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"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-5 (induced by source paper "optimizing rank based metrics with blackbox differentiation"):
  with members:
    - "learning with average precision training image retrieval with a listwise loss"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "towards accurate one stage object detection with ap loss"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "mediaeval 2018 predicting media memorability task"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-6 (induced by source paper "learning with average precision training image retrieval with a listwise loss"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "mihash online hashing with mutual information"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "vse improving visual semantic embeddings with hard negatives"
    - "fine tuning cnn image retrieval with no human annotation"
    - "no fuss distance metric learning using proxies"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"

Connected through hyperedge HE-7 (induced by source paper "from handcrafted to deep local features"):
  with members:
    - "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"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-8 (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"
    - "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"
    - "repeatability is not enough learning affine regions via discriminability"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

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)