human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cosface large margin cosine loss for deep face recognition"):
  Abstract: Face recognition has achieved revolutionary advancement owing to the advancement of the deep convolutional neural network (CNN). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, traditional softmax loss of deep CNN usually lacks the power of discrimination. To address this problem, recently several loss functions such as central loss \cite{centerloss}, large margin softmax loss \cite{lsoftmax}, and angular softmax loss \cite{sphereface} have been proposed. All these improvement algorithms share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we design a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as cosine loss by L2 normalizing both features and weight vectors to remove radial variation, based on which a cosine margin term \emph{$m$} is introduced to further maximize decision margin in angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. To test our approach, extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmark experiments, which confirms the effectiveness of our approach.

Includes members:
  Member "directional statistics based deep metric learning for image classification and retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation")
  Member "island loss for learning discriminative features in facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition")
  Member "beyond triplet loss a deep quadruplet network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "von mises fisher mixture model based deep learning application to face verification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation")
  Member "l2 constrained softmax loss for discriminative face verification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalizing face representation with unlabeled data")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "aggregated residual transformations for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "range loss for deep face recognition with long tail":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural architecture search for deep face recognition")

Question: Please tell me which class the source paper 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)