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 "privacynet semi adversarial networks for multi attribute face privacy"):
  Abstract: Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual's face image with high accuracy. However, this raises privacy concerns, especially when face images collected for biometric recognition purposes are used for attribute analysis without the person's consent. To address this problem, we develop a technique for imparting soft biometric privacy to face images via an image perturbation methodology. The image perturbation is undertaken using a GAN-based Semi-Adversarial Network (SAN) - referred to as PrivacyNet - that modifies an input face image such that it can be used by a face matcher for matching purposes but cannot be reliably used by an attribute classifier. Further, PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e.g., age and race), while allowing for other types of attributes to be extracted (e.g., gender). Extensive experiments using multiple face matchers, multiple age/gender/race classifiers, and multiple face datasets demonstrate the generalizability of the proposed multi-attribute privacy enhancing method across multiple face and attribute classifiers.

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 "some research problems in biometrics the future beckons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "rank consistent ordinal regression for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a neural approach to ordinal regression for the preventive assessment of developmental dyslexia")
  Member "mitigating information leakage in image representations a maximum entropy approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge")
  Member "right for the right reason training agnostic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rubi reducing unimodal biases in visual question answering")
  Member "anonymizing k facial attributes via adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised enhancement of soft biometric privacy with negative face recognition")
  Member "a hybrid model for identity obfuscation by face replacement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised enhancement of soft biometric privacy with negative 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)