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: "multispectral palmprint recognition using textural features"
  Abstract: In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features and used them for palmprint recognition. Co-occurrence matrix can be used for textural feature extraction. As classifiers, we have used the minimum distance classifier (MDC) and the weighted majority voting system (WMV). The proposed method is tested on a well-known multispectral palmprint dataset of 6000 samples and an accuracy rate of 99.96-100% is obtained for most scenarios which outperforms all previous works in multispectral palmprint recognition.

Connected through hyperedge HE-1 (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-2 (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"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-3 (induced by source paper "a pathophysiological model driven communication for dynamic distributed medical best practice guidance systems"):
  with members:
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"

Connected through hyperedge HE-4 (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"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "invariant scattering convolution networks"
    - "classification with scattering operators"

Connected through hyperedge HE-5 (induced by source paper "screen content image segmentation using sparse smooth decomposition"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "iris recognition using scattering transform and textural features"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "fingerprint recognition using translation invariant scattering network"
    - "screen content image segmentation using least absolute deviation fitting"
    - "a robust regression approach for background foreground segmentation"

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"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet 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 "screen content image segmentation using least absolute deviation fitting"):
  with members:
    - "a robust regression approach for background foreground segmentation"
    - "multispectral palmprint recognition using a hybrid feature"
    - "a geometric approach for fully automatic chromosome segmentation"

Connected through hyperedge HE-8 (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"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "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)