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: "iris recognition using scattering transform and textural features"
  Abstract: Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%.

Connected through hyperedge HE-1 (induced by source paper "deepiris iris recognition using a deep learning approach"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "face recognition using scattering convolutional network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "fingernet pushing the limits of fingerprint recognition using convolutional neural network"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "face recognition using scattering convolutional network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "iris gan learning to generate realistic iris images using convolutional gan"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "design design inspiration from generative networks"
    - "multicolumn networks for face recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "an experimental study of deep convolutional features for iris recognition"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "an experimental study of deep convolutional features for iris recognition"):
  with members:
    - "face recognition using scattering convolutional network"
    - "palmprint recognition using deep scattering convolutional network"
    - "deep residual learning for image recognition"
    - "texture synthesis using convolutional neural networks"
    - "fingerprint recognition using translation invariant scattering network"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

Connected through hyperedge HE-5 (induced by source paper "text extraction from texture images using masked signal decomposition"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "text flow a unified text detection system in natural scene images"
    - "textproposals a text specific selective search algorithm for word spotting in the wild"
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "fingerprint recognition using translation invariant scattering network"
    - "a hough transform based technique for text segmentation"

Connected through hyperedge HE-6 (induced by source paper "towards coordinated bandwidth adaptations for hundred scale 3d tele immersive systems"):
  with members:
    - "screen content image segmentation using sparse smooth decomposition"
    - "screen content image segmentation using least absolute deviation fitting"

Connected through hyperedge HE-7 (induced by source paper "face recognition using scattering convolutional network"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "person re identification using visual attention"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "palmprint recognition using deep scattering convolutional network"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "fingerprint recognition using translation invariant scattering network"
    - "a bayesian framework for sparse representation based 3 d human pose estimation"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

Connected through hyperedge HE-8 (induced by source paper "screen content image segmentation using sparse decomposition and total variation minimization"):
  with members:
    - "screen content image segmentation using sparse smooth decomposition"
    - "fingerprint recognition using translation invariant scattering network"
    - "screen content image segmentation using least absolute deviation fitting"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

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)