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: "deep learning for logo recognition"
  Abstract: In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried out on the FlickrLogos-32 database, and we evaluate the effect on recognition performance of synthetic versus real data augmentation, and image pre-processing. Moreover, we systematically investigate the benefits of different training choices such as class-balancing, sample-weighting and explicit modeling the background class (i.e. no-logo regions). Experimental results confirm the feasibility of the proposed method, that outperforms the methods in the state of the art.

Connected through hyperedge HE-1 (induced by source paper "a deep one shot network for query based logo retrieval"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "texture synthesis guided deep hashing for texture image retrieval"
    - "multi scale location aware kernel representation for object detection"
    - "open logo detection challenge"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "scalable deep learning logo detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "logo 2k a large scale logo dataset for scalable logo classification"):
  with members:
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "learning to navigate for fine grained classification"
    - "open logo detection challenge"
    - "scalable logo recognition using proxies"
    - "open set logo detection and retrieval"
    - "automatic graphic logo detection via fast region based convolutional networks"
    - "deep learning logo detection with data expansion by synthesising context"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "large scale open set deep logo detection"):
  with members:
    - "combination of multiple global descriptors for image retrieval"
    - "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"
    - "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 "image matters scalable detection of offensive and non compliant content logo in product images"):
  with members:
    - "a smart system for selection of optimal product images in e commerce"
    - "learning transferable architectures for scalable image recognition"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "ssd single shot multibox detector"
    - "deep learning logo detection with data expansion by synthesising context"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "scalable logo recognition using proxies"):
  with members:
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "open logo detection challenge"
    - "focal loss for dense object detection"
    - "squeeze and excitation networks"
    - "no fuss distance metric learning using proxies"
    - "open set logo detection and retrieval"
    - "image similarity using deep cnn and curriculum learning"

Connected through hyperedge HE-6 (induced by source paper "open logo detection challenge"):
  with members:
    - "scalable deep learning logo detection"
    - "open set logo detection and retrieval"
    - "synthesizing training data for object detection in indoor scenes"
    - "yolo9000 better faster stronger"
    - "generative image modeling using style and structure adversarial networks"
    - "deep learning logo detection with data expansion by synthesising context"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "learning discriminative 3d shape representations by view discerning networks"):
  with members:
    - "quality aware network for set to set recognition"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "fpnn field probing neural networks for 3d data"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "accurate image super resolution using very deep convolutional networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "learning illuminant estimation from object recognition"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "color constancy using cnns"

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)