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: "minimum margin loss for deep face recognition"
  Abstract: Face recognition has achieved great progress owing to the fast development of the deep neural network in the past a few years. As the baton in a deep neural network, a number of the loss functions have been proposed which significantly improve the state-of-the-art methods. In this paper, we proposed a new loss function called Minimum Margin Loss (MML) which aims at enlarging the margin of those over-close class centre pairs so as to enhance the discriminative ability of the deep features. MML supervises the training process together with the Softmax loss and the Centre loss, and also makes up the defect of Softmax + Centre loss. The experimental results on LFW and YTF datasets show that the proposed method achieves the state-of-the-art performance, which demonstrates the effectiveness of the proposed MML.

Connected through hyperedge HE-1 (induced by source paper "a performance comparison of loss functions for deep face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-2 (induced by source paper "hard mining loss based convolutional neural network for face recognition"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "survey of face detection on low quality images"
    - "cosface large margin cosine 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"

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)