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: "bags of local convolutional features for scalable instance search"
  Abstract: This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW). Assigning each local array of activations in a convolutional layer to a visual word produces an assignment map, a compact representation that relates regions of an image with a visual word. We use the assignment map for fast spatial reranking, obtaining object localizations that are used for query expansion. We demonstrate the suitability of the BoW representation based on local CNN features for instance retrieval, achieving competitive performance on the Oxford and Paris buildings benchmarks. We show that our proposed system for CNN feature aggregation with BoW outperforms state-of-the-art techniques using sum pooling at a subset of the challenging TRECVid INS benchmark.

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "s4l self supervised semi supervised learning"
    - "when does self supervision improve few shot learning"
    - "contrastive multiview coding"
    - "boosting few shot visual learning with self supervision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "momentum contrast for unsupervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "analyzing the dependency of convnets on spatial information"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deformable convnets v2 more deformable better results"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"

Connected through hyperedge HE-3 (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"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "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"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-4 (induced by source paper "approximating cnns with bag of local features models works surprisingly well on imagenet"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "scale coding bag of deep features for human attribute and action recognition"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "hcp a flexible cnn framework for multi label image classification"
    - "deep fishernet for object classification"

Connected through hyperedge HE-5 (induced by source paper "spatio semantic convnet based visual place recognition"):
  with members:
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "deep learning features at scale for visual place recognition"
    - "a taxonomy of deep convolutional neural nets for computer vision"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "convolutional neural network based image representation for visual loop closure detection"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (induced by source paper "learning non metric visual similarity for image retrieval"):
  with members:
    - "few example object detection with model communication"
    - "learning deep similarity models with focus ranking for fabric image retrieval"
    - "class weighted convolutional features for visual instance search"
    - "a simple neural network module for relational reasoning"
    - "no fuss distance metric learning using proxies"
    - "learning to compare relation network for few shot learning"
    - "faster r cnn features for instance search"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-7 (induced by source paper "an accurate retrieval through r mac descriptors for landmark recognition"):
  with members:
    - "class weighted convolutional features for visual instance search"
    - "a location aware embedding technique for accurate landmark recognition"
    - "saliency weighted convolutional features for instance search"
    - "context aware query image representation for particular object retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "sift meets cnn a decade survey of instance retrieval"
    - "cross dimensional weighting for aggregated deep convolutional features"

Connected through hyperedge HE-8 (induced by source paper "revisiting oxford and paris large scale image retrieval benchmarking"):
  with members:
    - "fine tuning cnn image retrieval with no human annotation"
    - "fast spectral ranking for similarity search"
    - "end to end learning of deep visual representations for image retrieval"
    - "large scale image retrieval with attentive deep local features"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "yfcc100m the new data in multimedia research"
    - "tilde a temporally invariant learned detector"

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)