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: "revisiting oxford and paris large scale image retrieval benchmarking"
  Abstract: In this paper we address issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the reliability of the ground truth. Three new protocols of varying difficulty are introduced. The protocols allow fair comparison between different methods, including those using a dataset pre-processing stage. For each dataset, 15 new challenging queries are introduced. Finally, a new set of 1M hard, semi-automatically cleaned distractors is selected. #R##N#An extensive comparison of the state-of-the-art methods is performed on the new benchmark. Different types of methods are evaluated, ranging from local-feature-based to modern CNN based methods. The best results are achieved by taking the best of the two worlds. Most importantly, image retrieval appears far from being solved.

Connected through hyperedge HE-1 (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-2 (induced by source paper "university 1652 a multi view multi source benchmark for drone based geo localization"):
  with members:
    - "building information modeling and classification by visual learning at a city scale"
    - "triplet based deep hashing network for cross modal retrieval"
    - "lending orientation to neural networks for cross view geo localization"
    - "optimal feature transport for cross view image geo localization"
    - "vision meets drones a challenge"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "cross view image matching for geo localization in urban environments"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "learning with average precision training image retrieval with a listwise loss"):
  with members:
    - "local descriptors optimized for average precision"
    - "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-5 (induced by source paper "why do these match explaining the behavior of image similarity models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "learning type aware embeddings for fashion compatibility"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "discriminative learning of latent features for zero shot recognition"
    - "a zero shot framework for sketch based image retrieval"

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"
    - "inloc indoor visual localization with dense matching and view synthesis"

Connected through hyperedge HE-7 (induced by source paper "sparse to dense hypercolumn matching for long term visual localization"):
  with members:
    - "lf net learning local features from images"
    - "leveraging deep visual descriptors for hierarchical efficient localization"
    - "improving nighttime retrieval based localization"
    - "scene coordinate and correspondence learning for image based localization"
    - "from coarse to fine robust hierarchical localization at large scale"
    - "night to day image translation for retrieval based localization"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-8 (induced by source paper "targeted mismatch adversarial attack query with a flower to retrieve the tower"):
  with members:
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "universal perturbation attack against image retrieval"
    - "open set adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

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)