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: "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
  Abstract: The iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described. Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.

Connected through hyperedge HE-1 (induced by source paper "sip segnet a deep convolutional encoder decoder network for joint semantic segmentation and extraction of sclera iris and pupil based on periocular region suppression"):
  with members:
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "a survey on periocular biometrics research"
    - "iris recognition after death"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "pupilnet v2 0 convolutional neural networks for cpu based real time robust pupil detection"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "human iris recognition in post mortem subjects study and database"
    - "3d gaze estimation from 2d pupil positions on monocular head mounted eye trackers"

Connected through hyperedge HE-2 (induced by source paper "deep representations for cross spectral ocular biometrics"):
  with members:
    - "an efficient and layout independent automatic license plate recognition system based on the yolo detector"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "periocular recognition using cnn features off the shelf"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"):
  with members:
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "a simple pooling based design for real time salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "salient object detection by lossless feature reflection"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "weakly supervised salient object detection using image labels"
    - "a fast and compact saliency score regression network based on fully convolutional network"

Connected through hyperedge HE-4 (induced by source paper "simultaneous iris and periocular region detection using coarse annotations"):
  with members:
    - "joint iris segmentation and localization using deep multi task learning framework"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "yolo9000 better faster stronger"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-5 (induced by source paper "ocular recognition databases and competitions a survey"):
  with members:
    - "deep representations for cross spectral ocular biometrics"
    - "simultaneous iris and periocular region detection using coarse annotations"
    - "openeds open eye dataset"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "a survey on periocular biometrics research"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-6 (induced by source paper "iris recognition with image segmentation employing retrained off the shelf deep neural networks"):
  with members:
    - "unconstrained iris segmentation using convolutional neural networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "dilated residual networks"
    - "yolo9000 better faster stronger"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "learning free iris segmentation revisited a first step toward fast volumetric operation over video samples"):
  with members:
    - "unconstrained iris segmentation using convolutional neural networks"
    - "data driven segmentation of post mortem iris images"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)