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 "fingerprint recognition using translation invariant scattering network"):
  Abstract: Fingerprint recognition has drawn a lot of attention during last decades. Different features and algorithms have been used for fingerprint recognition in the past. In this paper, a powerful image representation called scattering transform/network, is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to sift descriptors and the higher layers capture higher frequency content of the signal. After extraction of scattering features, their dimensionality is reduced by applying principal component analysis (PCA). At the end, multi-class SVM is used to perform template matching for the recognition task. The proposed scheme is tested on a well-known fingerprint database and has shown promising results with the best accuracy rate of 98\%.

Includes members:
  Member "screen content image segmentation using sparse smooth decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enhanced signal recovery via sparsity inducing image priors")
  Member "iris recognition using scattering transform and textural features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepiris iris recognition using a deep learning approach")
  Member "multi task image classification via collaborative hierarchical spike and slab priors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast low rank shared dictionary learning for image classification")
  Member "screen content image segmentation using least absolute deviation fitting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "an experimental study of deep convolutional features for iris recognition")
  Member "invariant scattering convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (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)