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: "a performance comparison of loss functions for deep face recognition"
  Abstract: The emergence of biometric tools and its increased usage in day to day devices has brought simplicity in the authentication process for the users as compared to the passwords and pattern locks being used. The ease of use of biometric reduces the manual work and helps in faster and automatic authentication. Among different biometric traits, the face is one which can be captured without much cooperation of users. Moreover, face recognition is one of the most widely publicized feature in the devices today and hence represents an important problem that should be studied with the utmost priority. As per the recent trends, the Convolutional Neural Network (CNN) based approaches are highly successful in many tasks of Computer Vision including face recognition. The loss function is used on the top of CNN to judge the goodness of any network. The loss functions play an important role in CNN training. Basically, it generates a huge loss, if the network does not perform well using the current parameter setting. In this paper, we present a performance comparison of different loss functions such as Cross-Entropy, Angular Softmax, Additive-Margin Softmax, ArcFace and Marginal Loss for face recognition. The experiments are conducted with two CNN architectures namely, ResNet and MobileNet. Two widely used face datasets namely, CASIA-Webface and MS-Celeb-1M are used for the training and benchmark Labeled Faces in the Wild (LFW) face dataset is used for the testing. The training and test results are analyzed in this paper.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search for deep face recognition"):
  with members:
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "psnet parametric sigmoid norm based cnn for face recognition"):
  with members:
    - "hard mining loss based convolutional neural network for face recognition"
    - "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
    - "average biased relu based cnn descriptor for improved face retrieval"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "hard mining loss based convolutional neural network for face recognition"):
  with members:
    - "survey of face detection on low quality images"
    - "cosface large margin cosine loss for deep face recognition"
    - "minimum margin loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "loop descriptor local optimal oriented pattern"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "evaluation of dataflow through layers of deep neural networks in classification and regression problems"):
  with members:
    - "scalable mutual information estimation using dependence graphs"
    - "opening the black box of deep neural networks via information"
    - "on loss functions for deep neural networks in classification"

Connected through hyperedge HE-5 (induced by source paper "a performance evaluation of convolutional neural networks for face anti spoofing"):
  with members:
    - "dual cnn models for unsupervised monocular depth estimation"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "mask r cnn"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "deep representations for iris face and fingerprint spoofing detection"

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)