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: "pigment melanin pattern for iris recognition"
  Abstract: Recognition of iris based on visible light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, which is unavailable in near-infrared (NIR) imaging. This is due to the biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive the feature code for each subject. An important question is how the melanin patterns, which are extracted from VL, are independent of the iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost recognition performance. We have collected our own database (UTIRIS), consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of chromophores and improves the iris recognition rate.

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"
    - "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"

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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a robust iris authentication system on gpu based edge devices using multi modalities learning model"):
  with members:
    - "deep learning on mobile devices a review"
    - "on the convergence of adam and beyond"
    - "stacked pooling improving crowd counting by boosting scale invariance"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "decoupled weight decay regularization"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "relevant features for gender classification in nir periocular images"):
  with members:
    - "predicting gender from iris texture may be harder than it seems"
    - "gender and ethnicity classification of iris images using deep class encoder"
    - "xgboost a scalable tree boosting system"
    - "iris codes classification using discriminant and witness directions"

Connected through hyperedge HE-6 (induced by source paper "sex prediction from periocular images across multiple sensors and spectra"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a survey on periocular biometrics research"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (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"
    - "segdensenet iris segmentation for pre and post cataract surgery"
    - "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"

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)