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: "combination of multiple global descriptors for image retrieval"
  Abstract: Recent studies in image retrieval task have shown that ensembling different models and combining multiple global descriptors lead to performance improvement. However, training different models for ensemble is not only difficult but also inefficient with respect to time or memory. In this paper, we propose a novel framework that exploits multiple global descriptors to get an ensemble-like effect while it can be trained in an end-to-end manner. The proposed framework is flexible and expandable by the global descriptor, CNN backbone, loss, and dataset. Moreover, we investigate the effectiveness of combining multiple global descriptors with quantitative and qualitative analysis. Our extensive experiments show that the combined descriptor outperforms a single global descriptor, as it can utilize different types of feature properties. In the benchmark evaluation, the proposed framework achieves the state-of-the-art performance on the CARS196, CUB200-2011, In-shop Clothes and Stanford Online Products on image retrieval tasks by a large margin compared to competing approaches. Our model implementations and pretrained models are publicly available.

Connected through hyperedge HE-1 (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"
    - "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"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "semi supervised feature level attribute manipulation for fashion image retrieval"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "hard aware point to set deep metric for person re identification"
    - "give me a hint navigating image databases using human in the loop feedback"
    - "began boundary equilibrium generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "squeeze and excitation networks"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "large scale open set deep logo detection"):
  with members:
    - "semantic granularity metric learning for visual search"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep face recognition a survey"
    - "attention based ensemble for deep metric learning"
    - "scalable logo recognition using proxies"
    - "deep learning for logo recognition"
    - "convolutional neural network architecture for geometric matching"
    - "receptive field block net for accurate and fast object detection"

Connected through hyperedge HE-4 (induced by source paper "team jl solution to google landmark recognition 2019"):
  with members:
    - "understanding and improving kernel local descriptors"
    - "attention aware generalized mean pooling for image retrieval"
    - "squeeze and excitation networks"
    - "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"
    - "deep image retrieval learning global representations for image search"
    - "large scale image retrieval with attentive deep local features"

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)