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: "average biased relu based cnn descriptor for improved face retrieval"
  Abstract: The convolutional neural networks (CNN) like AlexNet, GoogleNet, VGGNet, etc. have been proven as the very discriminative feature descriptor for many computer vision problems. The trained CNN model over one dataset performs reasonably well over another dataset of similar type and outperforms the hand-designed feature descriptor. The Rectified Linear Unit (ReLU) layer discards some information in order to introduce the non-linearity. In this paper, it is proposed that the discriminative ability of deep image representation using trained model can be improved by Average Biased ReLU (AB-ReLU) at last few layers. Basically, AB-ReLU improves the discriminative ability by two ways: 1) it also exploits some of the discriminative and discarded negative information of ReLU and 2) it kills the irrelevant and positive information used by ReLU. The VGGFace model already trained in MatConvNet over the VGG-Face dataset is used as the feature descriptor for face retrieval over other face datasets. The proposed approach is tested over six challenging unconstrained and robust face datasets like PubFig, LFW, PaSC, AR, etc. in retrieval framework. It is observed that AB-ReLU is consistently performed better than ReLU using VGGFace pretrained model over face datasets.

Connected through hyperedge HE-1 (induced by source paper "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"):
  with members:
    - "complex valued neural networks with non parametric activation functions"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "dfunet convolutional neural networks for diabetic foot ulcer classification"
    - "visualizing the loss landscape of neural nets"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "psnet parametric sigmoid norm based cnn for face recognition"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "hard mining loss based convolutional neural network for face recognition"
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "low resolution face recognition using a two branch deep convolutional neural network architecture"

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)