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: "human iris recognition in post mortem subjects study and database"
  Abstract: This paper presents a unique study of post-mortem human iris recognition and the first known to us database of near-infrared and visible-light iris images of deceased humans collected up to almost 17 days after death. We used four different iris recognition methods to analyze the dynamics of iris quality decay in short-term comparisons (samples collected up to 60 hours after death) and long-term comparisons (for samples acquired up to 407 hours after demise). This study shows that post-mortem iris recognition is possible and occasionally works even 17 days after death. These conclusions contradict a promulgated rumor that iris is unusable shortly after decease. We make this dataset publicly available to let others verify our findings and to research new aspects of this important and unfamiliar topic. We are not aware of any earlier papers offering post-mortem human iris images and such comprehensive analysis employing four different matchers.

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"
    - "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"
    - "iris recognition after death"
    - "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"
    - "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"
    - "iris recognition after death"
    - "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"
    - "iris recognition after death"
    - "perception of image features in post mortem iris recognition humans vs machines"
    - "post mortem human iris recognition"

Connected through hyperedge HE-5 (induced by source paper "iris recognition in cases of eye pathology"):
  with members:
    - "implications of ocular pathologies for iris recognition reliability"
    - "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"
    - "assessment of iris recognition reliability for eyes affected by ocular pathologies"
    - "linear regression analysis of template aging in iris biometrics"
    - "cataract influence on iris recognition performance"

Connected through hyperedge HE-6 (induced by source paper "ensemble of multi view learning classifiers for cross domain iris presentation attack detection"):
  with members:
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-7 (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"
    - "iris recognition after death"
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "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-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"
    - "an experimental study of deep convolutional features for iris recognition"
    - "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)