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: "an experimental study of deep convolutional features for iris recognition"
  Abstract: Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4\%, which outperforms the previous best result.

Connected through hyperedge HE-1 (induced by source paper "deep learning based feature extraction in iris recognition use existing models fine tune or train from scratch"):
  with members:
    - "domain specific human inspired binarized statistical image features for iris recognition"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "resfeats residual network based features for image classification"
    - "the nd iris 0405 iris image dataset"
    - "deep residual learning for image recognition"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep emotion facial expression recognition using attentional convolutional network"):
  with members:
    - "spatial temporal recurrent neural network for emotion recognition"
    - "ad net audio visual convolutional neural network for advertisement detection in videos"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "mask r cnn"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

Connected through hyperedge HE-3 (induced by source paper "thirdeye triplet based iris recognition without normalization"):
  with members:
    - "deepirisnet2 learning deep iriscodes from scratch for segmentation robust visible wavelength and near infrared iris recognition"
    - "learning free iris segmentation revisited a first step toward fast volumetric operation over video samples"
    - "influence of segmentation on deep iris recognition performance"
    - "iris recognition with image segmentation employing retrained off the shelf deep neural networks"
    - "unconstrained iris segmentation using convolutional neural networks"
    - "in defense of the triplet loss for person re identification"
    - "the nd iris 0405 iris image dataset"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "finger gan generating realistic fingerprint images using connectivity imposed gan"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "multicolumn networks for face recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "iris gan learning to generate realistic iris images using convolutional gan"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "masked signal decomposition using subspace representation and its applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "screen content image segmentation using robust regression and sparse decomposition"

Connected through hyperedge HE-5 (induced by source paper "iris gan learning to generate realistic iris images using convolutional gan"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "design design inspiration from generative networks"
    - "multicolumn networks for face recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "image segmentation using subspace representation and sparse decomposition"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "robust computation of linear models by convex relaxation"
    - "learning deconvolution network for semantic segmentation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "fully convolutional networks for semantic segmentation"
    - "stable image reconstruction using total variation minimization"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-7 (induced by source paper "pilot comparative study of different deep features for palmprint identification in low quality images"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "cnn features are also great at unsupervised classification"
    - "a comparative study of cnn bovw and lbp for classification of histopathological images"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "from facial parts responses to face detection a deep learning approach"
    - "very deep convolutional networks for large scale image recognition"
    - "on the power of joint wavelet dct features for multispectral palmprint recognition"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-8 (induced by source paper "perception of image features in post mortem iris recognition humans vs machines"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "presentation attack detection for cadaver iris"
    - "data driven segmentation of post mortem iris images"
    - "iris recognition after death"
    - "human iris recognition in post mortem subjects study and database"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"

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)