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: "open set logo detection and retrieval"
  Abstract: Current logo retrieval research focuses on closed set scenarios. We argue that the logo domain is too large for this strategy and requires an open set approach. To foster research in this direction, a large-scale logo dataset, called Logos in the Wild, is collected and released to the public. A typical open set logo retrieval application is, for example, assessing the effectiveness of advertisement in sports event broadcasts. Given a query sample in shape of a logo image, the task is to find all further occurrences of this logo in a set of images or videos. Currently, common logo retrieval approaches are unsuitable for this task because of their closed world assumption. Thus, an open set logo retrieval method is proposed in this work which allows searching for previously unseen logos by a single query sample. A two stage concept with separate logo detection and comparison is proposed where both modules are based on task specific CNNs. If trained with the Logos in the Wild data, significant performance improvements are observed, especially compared with state-of-the-art closed set approaches.

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"
    - "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-2 (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-3 (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"
    - "a simple neural attentive meta learner"
    - "deep learning logo detection with data expansion by synthesising context"
    - "deep feature consistent variational autoencoder"
    - "image to image translation with conditional adversarial networks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-4 (induced by source paper "omnitrack real time detection and tracking of objects text and logos in video"):
  with members:
    - "yolov3 an incremental improvement"
    - "detect or track towards cost effective video object detection tracking"
    - "simple online and realtime tracking"

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"
    - "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"
    - "deep learning for logo recognition"
    - "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 "scalable object detection for stylized objects"):
  with members:
    - "one shot face recognition by promoting underrepresented classes"
    - "training region based object detectors with online hard example mining"
    - "yolo9000 better faster stronger"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "facenet a unified embedding for face recognition and clustering"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "aggregating deep convolutional features for image retrieval"
    - "adam a method for stochastic optimization"

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)