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: "highly accurate multispectral palmprint recognition using statistical and wavelet features"
  Abstract: Palmprint is one of the most useful physiological biometrics that can be used as a powerful means in personal recognition systems. The major features of the palmprints are palm lines, wrinkles and ridges, and many approaches use them in different ways towards solving the palmprint recognition problem. Here we have proposed to use a set of statistical and wavelet-based features; statistical to capture the general characteristics of palmprints; and wavelet-based to find those information not evident in the spatial domain. Also we use two different classification approaches, minimum distance classifier scheme and weighted majority voting algorithm, to perform palmprint matching. The proposed method is tested on a well-known palmprint dataset of 6000 samples and has shown an impressive accuracy rate of 99.65\%-100\% for most scenarios.

Connected through hyperedge HE-1 (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"
    - "iris recognition using scattering transform and textural features"
    - "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"

Connected through hyperedge HE-2 (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"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "a bayesian framework for sparse representation based 3 d human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "palmprint recognition using deep scattering convolutional network"):
  with members:
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "fingerprint recognition using translation invariant scattering network"
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (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"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "screen content image segmentation using least absolute deviation fitting"

Connected through hyperedge HE-5 (induced by source paper "fingerprint recognition using translation invariant scattering network"):
  with members:
    - "screen content image segmentation using sparse smooth decomposition"
    - "iris recognition using scattering transform and textural features"
    - "multi task image classification via collaborative hierarchical spike and slab priors"
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "invariant scattering convolution networks"
    - "classification with scattering operators"

Connected through hyperedge HE-6 (induced by source paper "iris recognition using scattering transform and textural features"):
  with members:
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "a robust regression approach for background foreground segmentation"
    - "invariant scattering convolution networks"
    - "classification with scattering operators"

Connected through hyperedge HE-7 (induced by source paper "on the power of joint wavelet dct features for multispectral palmprint recognition"):
  with members:
    - "iris recognition using scattering transform and textural features"
    - "multi task image classification via collaborative hierarchical spike and slab priors"
    - "multispectral palmprint recognition using textural features"
    - "multispectral palmprint recognition using a hybrid feature"
    - "a geometric approach for fully automatic chromosome segmentation"

Connected through hyperedge HE-8 (induced by source paper "multispectral palmprint recognition using textural features"):
  with members:
    - "multispectral palmprint recognition using a hybrid feature"
    - "a geometric approach for fully automatic chromosome segmentation"

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)