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: "data driven segmentation of post mortem iris images"
  Abstract: This paper presents a method for segmenting iris images obtained from the deceased subjects, by training a deep convolutional neural network (DCNN) designed for the purpose of semantic segmentation. Post-mortem iris recognition has recently emerged as an alternative, or additional, method useful in forensic analysis. At the same time it poses many new challenges from the technological standpoint, one of them being the image segmentation stage, which has proven difficult to be reliably executed by conventional iris recognition methods. Our approach is based on the SegNet architecture, fine-tuned with 1,300 manually segmented post-mortem iris images taken from the Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in this paper show that this data-driven solution is able to learn specific deformations present in post-mortem samples, which are missing from alive irises, and offers a considerable improvement over the state-of-the-art, conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU) metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in this paper) averaged over subject-disjoint, multiple splits of the data into train and test subsets. This paper offers the first known to us method of automatic processing of post-mortem iris images. We offer source codes with the trained DCNN that perform end-to-end segmentation of post-mortem iris images, as described in this paper. Also, we offer binary masks corresponding to manual segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to facilitate development of alternative methods for post-mortem iris segmentation.

Connected through hyperedge HE-1 (induced by source paper "robust iris presentation attack detection fusing 2d and 3d information"):
  with members:
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "open source presentation attack detection baseline for iris recognition"
    - "iris presentation attack detection based on photometric stereo features"

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"
    - "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"
    - "presentation attack detection for cadaver iris"
    - "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 "learning free iris segmentation revisited a first step toward fast volumetric operation over video samples"):
  with members:
    - "unconstrained iris segmentation using convolutional neural networks"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "presentation attack detection for cadaver iris"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "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"
    - "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-6 (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"
    - "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)