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: "database of iris images acquired in the presence of ocular pathologies and assessment of iris recognition reliability for disease affected eyes"
  Abstract: This paper presents a database of iris images collected from disease affected eyes and an analysis related to the influence of ocular diseases on iris recognition reliability. For that purpose we have collected a database of iris images acquired for 91 different eyes during routine ophthalmology visits. This collection gathers samples for healthy eyes as well as those with various eye pathologies, including cataract, acute glaucoma, posterior and anterior synechiae, retinal detachment, rubeosis iridis, corneal vascularization, corneal grafting, iris damage and atrophy and corneal ulcers, haze or opacities. To our best knowledge this is the first database of such kind that will be made publicly available. In the analysis the data were divided into five groups of samples presenting similar anticipated impact on iris recognition: 1) healthy (no impact), 2) unaffected, clear iris (although the illness was detected), 3) geometrically distorted irides, 4) distorted iris tissue and 5) obstructed iris tissue. Three different iris recognition methods (MIRLIN, VeriEye and OSIRIS) were then used to find differences in average genuine and impostor comparison scores calculated for healthy eyes and those impacted by a disease. Specifically, we obtained significantly worse genuine comparison scores for all iris matchers and all disease-affected eyes when compared to a group of healthy eyes, what have a high potential of impacting false non-match rate.

Connected through hyperedge HE-1 (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"
    - "post mortem human iris recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-2 (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"
    - "post mortem human iris recognition"
    - "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-3 (induced by source paper "implications of ocular pathologies for iris recognition reliability"):
  with members:
    - "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-4 (induced by source paper "iris recognition under biologically troublesome conditions effects of aging diseases and post mortem changes"):
  with members:
    - "post mortem human iris recognition"
    - "assessment of iris recognition reliability for eyes affected by ocular pathologies"
    - "cataract influence on iris recognition performance"

Connected through hyperedge HE-5 (induced by source paper "post mortem human iris recognition"):
  with members:
    - "assessment of iris recognition reliability for eyes affected by ocular pathologies"

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)