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 "unsupervised enhancement of soft biometric privacy with negative face recognition"):
  Abstract: Current research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric templates of an individual. Since for many applications, these templates are expected to be used for recognition purposes only, this raises major privacy issues. Previous works focused on supervised privacy-enhancing solutions that require privacy-sensitive information about individuals and limit their application to the suppression of single and pre-defined attributes. Consequently, they do not take into account attributes that are not considered in the training. In this work, we present Negative Face Recognition (NFR), a novel face recognition approach that enhances the soft-biometric privacy on the template-level by representing face templates in a complementary (negative) domain. While ordinary templates characterize facial properties of an individual, negative templates describe facial properties that does not exist for this individual. This suppresses privacy-sensitive information from stored templates. Experiments are conducted on two publicly available datasets captured under controlled and uncontrolled scenarios on three privacy-sensitive attributes. The experiments demonstrate that our proposed approach reaches higher suppression rates than previous work, while maintaining higher recognition performances as well. Unlike previous works, our approach does not require privacy-sensitive labels and offers a more comprehensive privacy-protection not limited to pre-defined attributes.

Includes members:
  Member "flowsan privacy enhancing semi adversarial networks to confound arbitrary face based gender classifiers":
    also connected through other hyperedges:
      (none)
  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 "anonymizing k facial attributes via adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy")
  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 "facial attributes accuracy and adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "open dnn box by power side channel attack")
  Member "are facial attributes adversarially robust":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy")
  Member "ms celeb 1m a dataset and benchmark for large scale face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)