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: "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\%.

Connected through hyperedge HE-1 (induced by source paper "a study on wrist identification for forensic investigation"):
  with members:
    - "palmprint recognition using deep scattering convolutional network"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "invariant scattering convolution networks"
    - "point set registration coherent point drift"

Connected through hyperedge HE-2 (induced by source paper "finger gan generating realistic fingerprint images using connectivity imposed gan"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "multicolumn networks for face recognition"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "iris gan learning to generate realistic iris images using convolutional gan"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "masked signal decomposition using subspace representation and its applications"
    - "an experimental study of deep convolutional features for iris recognition"
    - "unpaired image to image translation using cycle consistent adversarial 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"
    - "iris recognition using scattering transform and textural features"
    - "texture synthesis using convolutional neural networks"
    - "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"
    - "iris recognition using scattering transform and textural features"
    - "a hough transform based technique for text segmentation"

Connected through hyperedge HE-6 (induced by source paper "cloud based deep learning of big eeg data for epileptic seizure prediction"):
  with members:
    - "screen content image segmentation using least absolute deviation fitting"
    - "union of low rank subspaces detector"

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"
    - "iris recognition using scattering transform and textural features"
    - "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 "palmprint recognition using deep scattering convolutional network"):
  with members:
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"
    - "invariant scattering convolution networks"

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)