human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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%.

Includes members:
  Member "screen content image segmentation using least absolute deviation fitting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enhanced signal recovery via sparsity inducing image priors")
  Member "multispectral palmprint recognition using textural features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "iris gan learning to generate realistic iris images using convolutional gan")
  Member "highly accurate multispectral palmprint recognition using statistical and wavelet features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an experimental study of deep convolutional features for iris recognition")
  Member "a robust regression approach for background foreground segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "screen content image segmentation using sparse smooth decomposition")
  Member "invariant scattering convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural networks")
  Member "classification with scattering operators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "geometric scattering for graph data analysis")

Question: Please tell me which class the source paper 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)