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 logo detection with data expansion by synthesising context"
  Abstract: Logo detection in unconstrained images is challenging, particularly when only very sparse labelled training images are accessible due to high labelling costs. In this work, we describe a model training image synthesising method capable of improving significantly logo detection performance when only a handful of (e.g., 10) labelled training images captured in realistic context are available, avoiding extensive manual labelling costs. Specifically, we design a novel algorithm for generating Synthetic Context Logo (SCL) training images to increase model robustness against unknown background clutters, resulting in superior logo detection performance. For benchmarking model performance, we introduce a new logo detection dataset TopLogo-10 collected from top 10 most popular clothing/wearable brandname logos captured in rich visual context. Extensive comparisons show the advantages of our proposed SCL model over the state-of-the-art alternatives for logo detection using two real-world logo benchmark datasets: FlickrLogo-32 and our new TopLogo-10.

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"
    - "deep learning for logo recognition"
    - "open set logo detection and retrieval"
    - "automatic graphic logo detection via fast region based convolutional networks"
    - "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"
    - "deep learning for logo recognition"
    - "convolutional neural network architecture for geometric matching"

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"
    - "deep learning for logo recognition"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "ssd single shot multibox detector"
    - "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 "variational prototyping encoder one shot learning with prototypical images"):
  with members:
    - "prototypical networks for few shot learning"
    - "understanding infographics through textual and visual tag prediction"
    - "co domain embedding using deep quadruplet networks for unseen traffic sign recognition"
    - "open set logo detection and retrieval"
    - "a simple neural attentive meta learner"
    - "deep feature consistent variational autoencoder"
    - "image to image translation with conditional adversarial networks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-6 (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"
    - "deep learning for logo recognition"
    - "squeeze and excitation networks"
    - "no fuss distance metric learning using proxies"
    - "open set logo detection and retrieval"

Connected through hyperedge HE-7 (induced by source paper "open logo detection challenge"):
  with members:
    - "scalable deep learning logo detection"
    - "deep learning for logo recognition"
    - "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"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "scalable deep learning logo detection"):
  with members:
    - "cooperative learning with visual attributes"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "webly supervised learning of convolutional networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "fast r cnn"
    - "transductive zero shot action recognition by word vector embedding"

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)