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: "logo net large scale deep logo detection and brand recognition with deep region based convolutional networks"
  Abstract: Logo detection from images has many applications, particularly for brand recognition and intellectual property protection. Most existing studies for logo recognition and detection are based on small-scale datasets which are not comprehensive enough when exploring emerging deep learning techniques. In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images. To facilitate research, LOGO-Net has two datasets: (i)"logos-18" consists of 18 logo classes, 10 brands, and 16,043 logo objects, and (ii) "logos-160" consists of 160 logo classes, 100 brands, and 130,608 logo objects. We describe the ideas and challenges for constructing such a large-scale database. Another key contribution of this work is to apply emerging deep learning techniques for logo detection and brand recognition tasks, and conduct extensive experiments by exploring several state-of-the-art deep region-based convolutional networks techniques for object detection tasks. The LOGO-net will be released at this http URL

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (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-5 (induced by source paper "logo synthesis and manipulation with clustered generative adversarial networks"):
  with members:
    - "optimal transport maps for distribution preserving operations on latent spaces of generative models"
    - "calibrating energy based generative adversarial networks"
    - "improved training of wasserstein gans"
    - "invertible conditional gans for image editing"
    - "neural photo editing with introspective adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "stacked generative adversarial networks"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-6 (induced by source paper "open set logo detection and retrieval"):
  with members:
    - "deep learning for logo recognition"
    - "the do s and don ts for cnn based face verification"
    - "a large scale dataset and benchmark for similar trademark retrieval"
    - "mask r cnn"
    - "automatic graphic logo detection via fast region based convolutional networks"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-7 (induced by source paper "deep learning logo detection with data expansion by synthesising context"):
  with members:
    - "automatic graphic logo detection via fast region based convolutional networks"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "webly supervised learning of convolutional networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "deeplogo hitting logo recognition with the deep neural network hammer"

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)