human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a deep one shot network for query based logo retrieval"):
  Abstract: Abstract   Logo detection in real-world scene images is an important problem with applications in advertisement and marketing. Existing general-purpose object detection methods require large training data with annotations for every logo class. These methods do not satisfy the incremental demand of logo classes necessary for practical deployment since it is practically impossible to have such annotated data for new unseen logo. In this work, we develop an easy-to-implement query-based logo detection and localization system by employing a one-shot learning technique using off the shelf neural network components. Given an image of a query logo, our model searches for logo within a given target image and predicts the possible location of the logo by estimating a binary segmentation mask. The proposed model consists of a conditional branch and a segmentation branch. The former gives a conditional latent representation of the given query logo which is combined with feature maps of the segmentation branch at multiple scales in order to obtain the matching location of the query logo in a target image. Feature matching between the latent query representation and multi-scale feature maps of segmentation branch using simple concatenation operation followed by 1 × 1 convolution layer makes our model scale-invariant. Despite its simplicity, our query-based logo retrieval framework achieved superior performance in FlickrLogos-32 and TopLogos-10 dataset over different existing baseline methods.

Includes members:
  Member "script identification in natural scene image and video frames using an attention based convolutional lstm network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "patch aggregator for scene text script identification")
  Member "handwriting recognition in low resource scripts using adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learn to augment joint data augmentation and network optimization for text recognition")
  Member "texture synthesis guided deep hashing for texture image retrieval":
    also connected through other hyperedges:
      (none)
  Member "multi scale location aware kernel representation for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection")
  Member "open logo detection challenge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "logo 2k a large scale logo dataset for scalable logo classification")
  Member "sg one similarity guidance network for one shot semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "scalable deep learning logo detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "open logo detection challenge")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

Question: Please tell me which class the source paper 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)