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: "particular object retrieval with integral max pooling of cnn activations"
  Abstract: Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets.

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 "deeply activated salient region for instance search"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "weakly supervised instance segmentation using class peak response"
    - "yolov3 an incremental improvement"
    - "instance search via instance level segmentation and feature representation"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "saliency weighted convolutional features for instance search"
    - "multiple instance detection network with online instance classifier refinement"

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"
    - "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-4 (induced by source paper "progressive local filter pruning for image retrieval acceleration"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "learning sparse sharing architectures for multiple tasks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "paraphrasing complex network network compression via factor transfer"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "fine tuning cnn image retrieval with no human annotation"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "automatic detection of rare pathologies in fundus photographs using few shot learning"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "generalizing from a few examples a survey on few shot learning"
    - "multi task learning for chest x ray abnormality classification on noisy labels"
    - "deep image mining for diabetic retinopathy screening"
    - "learning transferable architectures for scalable image recognition"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-6 (induced by source paper "enhancing feature invariance with learned image transformations for image retrieval"):
  with members:
    - "component based attention for large scale trademark retrieval"
    - "learning transferable architectures for scalable image recognition"
    - "deformable convolutional networks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "a large scale dataset and benchmark for similar trademark retrieval"
    - "proximal policy optimization algorithms"
    - "learning rotation invariant convolutional filters for texture classification"
    - "cross dimensional weighting for aggregated deep convolutional features"

Connected through hyperedge HE-7 (induced by source paper "affinity guided geometric semi supervised metric learning"):
  with members:
    - "deep metric learning with angular loss"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning via lifted structured feature embedding"
    - "going deeper with convolutions"
    - "matconvnet convolutional neural networks for matlab"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "a cnn based patent image retrieval method for design ideation"):
  with members:
    - "technet technology semantic network based on patent data"
    - "yolov3 an incremental improvement"
    - "deep image retrieval learning global representations for image search"
    - "modeling of technological performance trends using design theory"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "very deep convolutional networks for large scale image recognition"

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)