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: "segdensenet iris segmentation for pre and post cataract surgery"
  Abstract: Cataract is caused due to various factors such as age, trauma, genetics, smoking and substance consumption, and radiation. It is one of the major common ophthalmic diseases worldwide which can potentially affect iris-based biometric systems. India, which hosts the largest biometrics project in the world, has about 8 million people undergoing cataract surgery annually. While existing research shows that cataract does not have a major impact on iris recognition, our observations suggest that the iris segmentation approaches are not well equipped to handle cataract or post cataract surgery cases. Therefore, failure in iris segmentation affects the overall recognition performance. This paper presents an efficient iris segmentation algorithm with variations due to cataract and post cataract surgery. The proposed algorithm, termed as SegDenseNet, is a deep learning algorithm based on DenseNets. The experiments on the IIITD Cataract database show that improving the segmentation enhances the identification by up to 25% across different sensors and matchers.

Connected through hyperedge HE-1 (induced by source paper "deep neural network and data augmentation methodology for off axis iris segmentation in wearable headsets"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "a survey of augmented reality navigation"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "gesture based continuous authentication for wearable devices the google glass case"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "fully convolutional networks and generative adversarial networks applied to sclera segmentation"):
  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"
    - "semantic segmentation using adversarial networks"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "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)