human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
  Abstract: In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In order to train this autoencoder, we propose a novel training scheme, referred to as semi-adversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of a pseudo gender classifier and a pseudo face matcher to the autoencoder. The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted. Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images.

Connected through hyperedge HE-1 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge"):
  with members:
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "fairface face attribute dataset for balanced race gender and age"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "rank consistent ordinal regression for neural networks"
    - "mitigating information leakage in image representations a maximum entropy approach"
    - "right for the right reason training agnostic networks"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "a hybrid model for identity obfuscation by face replacement"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"

Connected through hyperedge HE-3 (induced by source paper "unsupervised enhancement of soft biometric privacy with negative face recognition"):
  with members:
    - "flowsan privacy enhancing semi adversarial networks to confound arbitrary face based gender classifiers"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "facial attributes accuracy and adversarial robustness"
    - "are facial attributes adversarially robust"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "post comparison mitigation of demographic bias in face recognition using fair score normalization"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "fairness through awareness"

Connected through hyperedge HE-5 (induced by source paper "mitigate bias in face recognition using skewness aware reinforcement learning"):
  with members:
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep face recognition a survey"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "racial faces in the wild reducing racial bias by information maximization adaptation network"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-6 (induced by source paper "sensitivenets learning agnostic representations with application to face recognition"):
  with members:
    - "right for the right reason training agnostic networks"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "yfcc100m the new data in multimedia research"
    - "simultaneous deep transfer across domains and tasks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "on the global optima of kernelized adversarial representation learning"):
  with members:
    - "mitigating information leakage in image representations a maximum entropy approach"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "the numerics of gans"
    - "adversarial discriminative domain adaptation"
    - "controllable invariance through adversarial feature learning"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-8 (induced by source paper "evaluating the effectiveness of automated identity masking aim methods with human perception and a deep convolutional neural network cnn"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "the do s and don ts for cnn based face verification"
    - "face deidentification with generative deep neural networks"
    - "triplet probabilistic embedding for face verification and clustering"
    - "deep residual learning for image recognition"
    - "deeply learned face representations are sparse selective and robust"

Question: Please tell me which class the center node 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)