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: "the nd iris 0405 iris image dataset"
  Abstract: The Computer Vision Research Lab at the University of Notre Dame began collecting iris images in the spring semester of 2004. The initial data collections used an LG 2200 iris imaging system for image acquisition. Image datasets acquired in 2004-2005 at Notre Dame with this LG 2200 have been used in the ICE 2005 and ICE 2006 iris biometric evaluations. The ICE 2005 iris image dataset has been distributed to over 100 research groups around the world. The purpose of this document is to describe the content of the ND-IRIS-0405 iris image dataset. This dataset is a superset of the iris image datasets used in ICE 2005 and ICE 2006. The ND 2004-2005 iris image dataset contains 64,980 images corresponding to 356 unique subjects, and 712 unique irises. The age range of the subjects is 18 to 75 years old. 158 of the subjects are female, and 198 are male. 250 of the subjects are Caucasian, 82 are Asian, and 24 are other ethnicities.

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 "deep learning based feature extraction in iris recognition use existing models fine tune or train from scratch"):
  with members:
    - "domain specific human inspired binarized statistical image features for iris recognition"
    - "the impact of preprocessing on deep representations for iris recognition on unconstrained environments"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "an experimental study of deep convolutional features for iris recognition"
    - "resfeats residual network based features for image classification"
    - "deep residual learning for image recognition"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "learning to authenticate with deep multibiometric hashing and neural network decoding"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "deep learning methods for improved decoding of linear codes"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "multibiometric secure system based on deep learning"
    - "rnn decoding of linear block codes"
    - "neural offset min sum decoding"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-4 (induced by source paper "thirdeye triplet based iris recognition without normalization"):
  with members:
    - "deepirisnet2 learning deep iriscodes from scratch for segmentation robust visible wavelength and near infrared iris recognition"
    - "learning free iris segmentation revisited a first step toward fast volumetric operation over video samples"
    - "influence of segmentation on deep iris recognition performance"
    - "iris recognition with image segmentation employing retrained off the shelf deep neural networks"
    - "unconstrained iris segmentation using convolutional neural networks"
    - "in defense of the triplet loss for person re identification"
    - "an experimental study of deep convolutional features for iris recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "iris verification with convolutional neural network"):
  with members:
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-6 (induced by source paper "iris verification with convolutional neural network and unit circle layer"):
  with members:
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-7 (induced by source paper "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "deep cross polarimetric thermal to visible face recognition"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "few shot adversarial domain adaptation"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "generalized bilinear deep convolutional neural networks for multimodal biometric identification"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

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)