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: "dctnet a simple learning free approach for face recognition"
  Abstract: PCANet was proposed as a lightweight deep learning network that mainly leverages Principal Component Analysis (PCA) to learn multistage filter banks followed by binarization and block-wise histograming. PCANet was shown worked surprisingly well in various image classification tasks. However, PCANet is data-dependence hence inflexible. In this paper, we proposed a data-independence network, dubbed DCTNet for face recognition in which we adopt Discrete Cosine Transform (DCT) as filter banks in place of PCA. This is motivated by the fact that 2D DCT basis is indeed a good approximation for high ranked eigenvectors of PCA. Both 2D DCT and PCA resemble a kind of modulated sine-wave patterns, which can be perceived as a bandpass filter bank. DCTNet is free from learning as 2D DCT bases can be computed in advance. Besides that, we also proposed an effective method to regulate the block-wise histogram feature vector of DCTNet for robustness. It is shown to provide surprising performance boost when the probe image is considerably different in appearance from the gallery image. We evaluate the performance of DCTNet extensively on a number of benchmark face databases and being able to achieve on par with or often better accuracy performance than PCANet.

Connected through hyperedge HE-1 (induced by source paper "multi fold gabor pca and ica filter convolution descriptor for face recognition"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "deepid3 face recognition with very deep neural networks"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "a comprehensive survey on pose invariant face recognition"
    - "pcanet a simple deep learning baseline for image classification"
    - "effective face frontalization in unconstrained images"
    - "large scale strongly supervised ensemble metric learning with applications to face verification and retrieval"

Connected through hyperedge HE-2 (induced by source paper "momentsnet a simple learning free method for binary image recognition"):
  with members:
    - "deep residual learning for image recognition"
    - "pcanet a simple deep learning baseline for image classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "dctnet and pcanet for acoustic signal feature extraction"):
  with members:
    - "pcanet a simple deep learning baseline for image classification"
    - "deep scattering spectrum"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "modified dctnet for audio signals classification"):
  with members:
    - "pcanet a simple deep learning baseline for image classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "theano new features and speed improvements"

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)