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: "presentation attack detection for cadaver iris"
  Abstract: This paper presents a deep-learning-based method for iris presentation attack detection (PAD) when iris images are obtained from deceased people. Our approach is based on the VGG-16 architecture fine-tuned with a database of 574 post-mortem, near-infrared iris images from the Warsaw-BioBase-PostMortem-Iris-v1 database, complemented by a dataset of 256 images of live irises, collected within the scope of this study. Experiments described in this paper show that our approach is able to correctly classify iris images as either representing a live or a dead eye in almost 99% of the trials, averaged over 20 subject-disjoint, train/test splits. We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0%@BPCER=1% (Attack Presentation and Bona Fide Presentation Classification Error Rates, respectively) when only post-mortem samples collected at least 16 hours post-mortem are considered. Since acquisitions of ante- and post-mortem samples differ significantly, we applied countermeasures to minimize bias in our classification methodology caused by image properties that are not related to the PAD. This included using the same iris sensor in collection of ante- and post-mortem samples, and analysis of class activation maps to ensure that discriminant iris regions utilized by our classifier are related to properties of the eye, and not to those of the acquisition protocol. This paper offers the first known to us PAD method in a post-mortem setting, together with an explanation of the decisions made by the convolutional neural network. Along with the paper we offer source codes, weights of the trained network, and a dataset of live iris images to facilitate reproducibility and further research.

Connected through hyperedge HE-1 (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"
    - "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"
    - "human iris recognition in post mortem subjects study and database"
    - "fully convolutional siamese networks for object tracking"

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"
    - "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"
    - "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"
    - "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"
    - "post mortem human iris recognition"

Connected through hyperedge HE-4 (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"
    - "data driven segmentation of post mortem iris images"
    - "iris recognition after death"
    - "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)