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: "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.

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:
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "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"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "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 "eyenet attention based convolutional encoder decoder network for eye region segmentation"):
  with members:
    - "openeds open eye dataset"
    - "skeletonnet shape pixel to skeleton pixel"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "cbam convolutional block attention module"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "eye tracking for everyone"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "openeds open eye dataset"):
  with members:
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "billion scale similarity search with gpus"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "eye tracking for everyone"
    - "learning from simulated and unsupervised images through adversarial training"
    - "labelled pupils in the wild a dataset for studying pupil detection in unconstrained environments"
    - "a survey of semantic segmentation"

Connected through hyperedge HE-5 (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"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "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-6 (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"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "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-7 (induced by source paper "eye semantic segmentation with a lightweight model"):
  with members:
    - "openeds open eye dataset"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "averaging weights leads to wider optima and better generalization"
    - "squeeze and excitation networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-8 (induced by source paper "robust iris segmentation based on fully convolutional networks and generative adversarial networks"):
  with members:
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "road segmentation in sar satellite images with deep fully convolutional neural networks"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "semantic segmentation using adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "you only look once unified real time object detection"

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)