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: "deeplogo hitting logo recognition with the deep neural network hammer"
  Abstract: Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto's service for marketers to track their brands in user-generated images, and LogoGrab's mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.

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"
    - "deep learning logo detection with data expansion by synthesising context"

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"
    - "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"

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"
    - "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-6 (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"
    - "deep learning logo detection with data expansion by synthesising context"
    - "improved techniques for training gans"

Connected through hyperedge HE-7 (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"
    - "deep learning logo detection with data expansion by synthesising context"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"):
  with members:
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "eie efficient inference engine on compressed deep neural network"
    - "dsd dense sparse dense training for deep neural networks"
    - "shallow networks for high accuracy road object detection"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

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)