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: "face recognition using scattering convolutional network"
  Abstract: Face recognition has been an active research area in the past few decades. In general, face recognition can be very challenging due to variations in viewpoint, illumination, facial expression, etc. Therefore it is essential to extract features which are invariant to some or all of these variations. Here a new image representation, called scattering transform/network, has been used to extract features from faces. The scattering transform is a kind of convolutional network which provides a powerful multi-layer representation for signals. After extraction of scattering features, PCA is applied to reduce the dimensionality of the data and then a multi-class support vector machine is used to perform recognition. The proposed algorithm has been tested on three face datasets and achieved a very high recognition rate.

Connected through hyperedge HE-1 (induced by source paper "deepiris iris recognition using a deep learning approach"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "iris recognition using scattering transform and textural features"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "fingernet pushing the limits of fingerprint recognition using convolutional neural network"):
  with members:
    - "deep emotion facial expression recognition using attentional convolutional network"
    - "a deep unsupervised learning approach toward mtbi identification using diffusion mri"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "iris recognition using scattering transform and textural features"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "an experimental study of deep convolutional features for iris recognition"):
  with members:
    - "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"
    - "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)