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 "fully convolutional networks and generative adversarial networks applied to sclera segmentation"):
  Abstract: Due to the world’s demand for security systems, biometrics can be seen as an important topic of research in computer vision. One of the biometric forms that has been gaining attention is the recognition based on sclera. The initial and paramount step for performing this type of recognition is the segmentation of the region of interest, i.e. the sclera. In this context, two approaches for such task based on the Fully Convolutional Network (FCN) and on Generative Adversarial Network (GAN) are introduced in this work. FCN is similar to a common convolution neural network, however the fully connected layers (i.e., the classification layers) are removed from the end of the network and the output is generated by combining the output of pooling layers from different convolutional ones. The GAN is based on the game theory, where we have two networks competing with each other to generate the best segmentation. In order to perform fair comparison with baselines and quantitative and objective evaluations of the proposed approaches, we provide to the scientific community new 1,300 manually segmented images from two databases1. The experiments are performed on the UBIRIS.v2 and MICHE databases and the best performing configurations of our propositions achieved F-score's measures of 87.48% and 88.32%, respectively.

Includes members:
  Member "a benchmark for iris location and a deep learning detector evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep representations for cross spectral ocular biometrics")
  Member "segdensenet iris segmentation for pre and post cataract surgery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep neural network and data augmentation methodology for off axis iris segmentation in wearable headsets")
  Member "an application of cascaded 3d fully convolutional networks for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "a robust real time automatic license plate recognition based on the yolo detector":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep representations for cross spectral ocular biometrics")
  Member "road segmentation in sar satellite images with deep fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image conditioned graph generation for road network extraction")
  Member "semantic segmentation using adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "spatially supervised recurrent convolutional neural networks for visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deformable object tracking with gated fusion")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)