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: "detect to retrieve efficient regional aggregation for image search"
  Abstract: Retrieving object instances among cluttered scenes efficiently requires compact yet comprehensive regional image representations. Intuitively, object semantics can help build the index that focuses on the most relevant regions. However, due to the lack of bounding-box datasets for objects of interest among retrieval benchmarks, most recent work on regional representations has focused on either uniform or class-agnostic region selection. In this paper, we first fill the void by providing a new dataset of landmark bounding boxes, based on the Google Landmarks dataset, that includes $86k$ images with manually curated boxes from $15k$ unique landmarks. Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods. In addition, we introduce a novel regional aggregated selective match kernel (R-ASMK) to effectively combine information from detected regions into an improved holistic image representation. R-ASMK boosts image retrieval accuracy substantially with no dimensionality increase, while even outperforming systems that index image regions independently. Our complete image retrieval system improves upon the previous state-of-the-art by significant margins on the Revisited Oxford and Paris datasets. Code and data available at the project webpage: this https URL.

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 "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"
    - "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"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-3 (induced by source paper "an analysis of object embeddings for image retrieval"):
  with members:
    - "learning rich features for image manipulation detection"
    - "do better imagenet models transfer better"
    - "deep learning for generic object detection a survey"
    - "dgc net dense geometric correspondence network"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "the ps battles dataset an image collection for image manipulation detection"
    - "sniper efficient multi scale training"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-4 (induced by source paper "a benchmark on tricks for large scale image retrieval"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "semi supervised feature level attribute manipulation for fashion image retrieval"
    - "combination of multiple global descriptors for image retrieval"
    - "multigrain a unified image embedding for classes and instances"
    - "efficient nearest neighbors search for large scale landmark recognition"
    - "bag of tricks for image classification with convolutional neural networks"
    - "batch dropblock network for person re identification and beyond"
    - "efficient image retrieval via decoupling diffusion into online and offline processing"

Connected through hyperedge HE-5 (induced by source paper "actnet end to end learning of feature activations and multi stream aggregation for effective instance image retrieval"):
  with members:
    - "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "attention aware generalized mean pooling for image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "aggregated residual transformations for deep neural networks"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-6 (induced by source paper "large scale landmark recognition via deep metric learning"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "billion scale similarity search with gpus"
    - "visual search at ebay"
    - "squeeze and excitation networks"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"
    - "large scale image retrieval with attentive deep local features"

Connected through hyperedge HE-7 (induced by source paper "challenging deep image descriptors for retrieval in heterogeneous iconographic collections"):
  with members:
    - "d2 net a trainable cnn for joint detection and description of local features"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "fine tuning cnn image retrieval with no human annotation"
    - "lift learned invariant feature transform"
    - "large scale image retrieval with attentive deep local features"
    - "tilde a temporally invariant learned detector"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-8 (induced by source paper "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"):
  with members:
    - "from selective deep convolutional features to compact binary representations for image retrieval"
    - "deep learning features at scale for visual place recognition"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "sift meets cnn a decade survey of instance retrieval"
    - "visual place recognition using landmark distribution descriptors"
    - "cross convolutional layer pooling for image recognition"
    - "spatial transformer networks"
    - "particular object retrieval with integral max pooling of cnn activations"

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)