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: "post mortem human iris recognition"
  Abstract: This paper presents a unique analysis of post-mortem human iris recognition. Post-mortem human iris images were collected at the university mortuary in three sessions separated by approximately 11 hours, with the first session organized from 5 to 7 hours after demise. Analysis performed for four independent iris recognition methods shows that the common claim of the iris being useless for biometric identification soon after death is not entirely true. Since the pupil has a constant and neutral dilation after death (the so called “cadaveric position”), this makes the iris pattern perfectly visible from the standpoint of dilation. We found that more than 90% of irises are still correctly recognized when captured a few hours after death, and that serious iris deterioration begins approximately 22 hours later, since the recognition rate drops to a range of 13.3–73.3% (depending on the method used) when the cornea starts to be cloudy. There were only two failures to enroll (out of 104 images) observed for only a single method (out of four employed in this study). These findings show that the dynamics of post-mortem changes to the iris that are important for biometric identification are much more moderate than previously believed. To the best of our knowledge, this paper presents the first experimental study of how iris recognition works after death, and we hope that these preliminary findings will stimulate further research in this area.

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 "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"
    - "human iris recognition in post mortem subjects study and database"
    - "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-3 (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"
    - "human iris recognition in post mortem subjects study and database"

Connected through hyperedge HE-4 (induced by source paper "iris recognition in cases of eye pathology"):
  with members:
    - "implications of ocular pathologies for iris recognition reliability"
    - "human iris recognition in post mortem subjects study and database"
    - "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-5 (induced by source paper "data driven segmentation of post mortem iris images"):
  with members:
    - "a review on deep learning techniques applied to semantic segmentation"
    - "human iris recognition in post mortem subjects study and database"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "presentation attack detection for iris recognition an assessment of the state of the art"):
  with members:
    - "cross spectral iris recognition for mobile applications using high quality color images"
    - "ghclnet a generalized hierarchically tuned contact lens detection network"
    - "beat an open source web based open science platform"
    - "human iris recognition in post mortem subjects study and database"
    - "deep representations for iris face and fingerprint spoofing detection"

Connected through hyperedge HE-7 (induced by source paper "iris recognition after death"):
  with members:
    - "cross spectral iris recognition for mobile applications using high quality color images"
    - "implications of ocular pathologies for iris recognition reliability"
    - "iris recognition with a database of iris images obtained in visible light using smartphone camera"
    - "human iris recognition in post mortem subjects study and database"

Connected through hyperedge HE-8 (induced by source paper "iris recognition under biologically troublesome conditions effects of aging diseases and post mortem changes"):
  with members:
    - "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"
    - "cataract influence on iris recognition performance"

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)