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 "post comparison mitigation of demographic bias in face recognition using fair score normalization"):
  Abstract: Current face recognition systems achieved high progress on several benchmark tests. Despite this progress, recent works showed that these systems are strongly biased against demographic sub-groups. Consequently, an easily integrable solution is needed to reduce the discriminatory effect of these biased systems. Previous work introduced fairness-enhancing solutions that strongly degrades the overall system performance. In this work, we propose a novel fair score normalization approach that is specifically designed to reduce the effect of bias in face recognition and subsequently lead to a significant overall performance boost. Our hypothesis is built on the notation of individual fairness by designing a normalization approach that leads to treating "similar" individuals "similarly". Experiments were conducted on two publicly available datasets captured under controlled and in-the-wild circumstances. The results show that our fair normalization approach enhances the overall performance by up to 14.8% under intermediate false match rate settings and up to 30.7% under high security settings. Our proposed approach significantly reduces the errors of all demographic groups, and thus reduce bias. Especially under in-the-wild conditions, we demonstrated that our fair normalization method improves the recognition performance of the effected population sub-groups by 31.6%. Unlike previous work, our proposed fairness-enhancing solution does not require demographic information about the individuals, leads to an overall performance boost, and can be easily integrated in existing biometric systems.

Includes members:
  Member "sensitivenets learning agnostic representations with application to face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "turning a blind eye explicit removal of biases and variation from deep neural network embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy")
  Member "semi adversarial networks convolutional autoencoders for imparting privacy to face images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "ms celeb 1m a dataset and benchmark for large scale face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fairness through awareness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithmic fairness")

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)