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 "learning mappings onto regularized latent spaces for biometric authentication"):
  Abstract: We propose a novel architecture for generic biometric authentication based on deep neural networks: RegNet. Differently from other methods, RegNet learns a mapping of the input biometric traits onto a target distribution in a well-behaved space in which users can be separated by means of simple and tunable boundaries. More specifically, authorized and unauthorized users are mapped onto two different and well behaved Gaussian distributions. The novel approach of learning the mapping instead of the boundaries further avoids the problem encountered in typical classifiers for which the learnt boundaries may be complex and difficult to analyze. RegNet achieves high performance in terms of security metrics such as Equal Error Rate (EER), False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR). The experiments we conducted on publicly available datasets of face and fingerprint confirm the effectiveness of the proposed system.

Includes members:
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "classification regions of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study")
  Member "mixup beyond empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learning face representation from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

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)