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: "palmprint recognition using deep scattering convolutional network"
  Abstract: Palmprint recognition has drawn a lot of attention during the recent years. Many algorithms have been proposed for palmprint recognition in the past, majority of them being based on features extracted from the transform domain. Many of these transform domain features are not translation or rotation invariant, and therefore a great deal of preprocessing is needed to align the images. In this paper, a powerful image representation, called scattering network/transform, is used for palmprint recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering network captures similar features to SIFT descriptors and the higher-layer features capture higher-frequency content of the signal which are lost in SIFT and other similar descriptors. After extraction of the scattering features, their dimensionality is reduced by applying principal component analysis (PCA) which reduces the computational complexity of the recognition task. Two different classifiers are used for recognition: multi-class SVM and minimum-distance classifier. The proposed scheme has been tested on a well-known palmprint database and achieved accuracy rate of 99.95% and 100% using minimum distance classifier and SVM respectively.

Connected through hyperedge HE-1 (induced by source paper "towards unconstrained palmprint recognition on consumer devices a literature review"):
  with members:
    - "palmprint recognition in uncontrolled and uncooperative environment"
    - "a study on wrist identification for forensic investigation"
    - "pilot comparative study of different deep features for palmprint identification in low quality images"
    - "11k hands gender recognition and biometric identification using a large dataset of hand images"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "convolutional pose machines"

Connected through hyperedge HE-2 (induced by source paper "palmprint recognition in uncontrolled and uncooperative environment"):
  with members:
    - "end to end weakly supervised semantic alignment"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "pose invariant 3d face alignment"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-3 (induced by source paper "a study on wrist identification for forensic investigation"):
  with members:
    - "fingerprint recognition using translation invariant scattering 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-4 (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-5 (induced by source paper "an experimental study of deep convolutional features for iris recognition"):
  with members:
    - "face recognition using 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"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

Connected through hyperedge HE-6 (induced by source paper "superpixel based segmentation and classification of polyps in wireless capsule endoscopy"):
  with members:
    - "screen content image segmentation using robust regression and sparse decomposition"

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"
    - "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"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

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)