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: "iris recognition after death"
  Abstract: This paper presents a comprehensive study of post-mortem human iris recognition carried out for 1,200 near-infrared and 1,787 visible-light samples collected from 37 deceased individuals kept in the mortuary conditions. We used four independent iris recognition methods (three commercial and one academic) to analyze genuine and impostor comparison scores and check the dynamics of iris quality decay over a period of up to 814 hours after death. This study shows that post-mortem iris recognition may be close-to-perfect approximately 5 to 7 hours after death and occasionally is still viable even 21 days after death. These conclusions contradict the statements present in past literature that the iris is unusable as a biometrics shortly after death, and show that the dynamics of post-mortem changes to the iris that are important for biometric identification are more moderate than previously hypothesized. The paper contains a thorough medical commentary that helps to understand which post-mortem metamorphoses of the eye may impact the performance of automatic iris recognition. We also show that post-mortem iris recognition works equally well for images taken in near-infrared and when the red channel of visible-light sample is used. However, cross-wavelength matching presents significantly worse performance. This paper conforms to reproducible research and the database used in this study is made publicly available to facilitate research of post-mortem iris recognition. To our knowledge, this paper offers the most comprehensive evaluation of post-mortem iris recognition and the largest database of post-mortem iris images.

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"
    - "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 "post mortem iris recognition resistant to biological eye decay processes"):
  with members:
    - "domain specific human inspired binarized statistical image features for iris recognition"
    - "presentation attack detection for cadaver iris"
    - "perception of image features in post mortem iris recognition humans vs machines"
    - "object cosegmentation using deep siamese network"
    - "superpoint self supervised interest point detection and description"
    - "lift learned invariant feature transform"
    - "human iris recognition in post mortem subjects study and database"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-3 (induced by source paper "post mortem iris recognition with deep learning based image segmentation"):
  with members:
    - "iris recognition with image segmentation employing retrained off the shelf deep neural networks"
    - "presentation attack detection for cadaver iris"
    - "data driven segmentation of post mortem iris images"
    - "human iris recognition in post mortem subjects study and database"
    - "post mortem human iris recognition"
    - "database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease affected eyes"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "post mortem iris decomposition and its dynamics in morgue conditions"):
  with members:
    - "post mortem iris recognition with deep learning based image segmentation"
    - "domain specific human inspired binarized statistical image features for iris recognition"
    - "presentation attack detection for cadaver iris"
    - "data driven segmentation of post mortem iris images"
    - "perception of image features in post mortem iris recognition humans vs machines"
    - "human iris recognition in post mortem subjects study and database"
    - "post mortem human iris recognition"

Connected through hyperedge HE-5 (induced by source paper "some research problems in biometrics the future beckons"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"

Connected through hyperedge HE-6 (induced by source paper "presentation attack detection for cadaver iris"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "data driven segmentation of post mortem iris images"
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "human iris recognition in post mortem subjects study and database"
    - "exploring models and data for image question answering"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-7 (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"
    - "an experimental study of deep convolutional features for iris recognition"
    - "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)