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: "sensitivenets learning agnostic representations with application to face recognition"
  Abstract: This work proposes a new neural network feature representation that help to leave out sensitive information in the decision-making process of pattern recognition and machine learning algorithms. The aim of this work is to develop a learning method capable to remove certain information from the feature space without drop of performance in a recognition task based on that feature space. Our work is in part motivated by the new international regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data of users. Our method is based on a triplet loss learning generalization that introduces a sensitive information removal process. The method is evaluated on face recognition technologies using state-of-the-art algorithms and publicly available benchmarks. In addition, we present a new annotation dataset with balanced distribution between genders and ethnic origins. The dataset includes more than 120K images from 24K identities with variety of poses, image quality, facial expressions, and illumination. The experiments demonstrate that it is possible to reduce sensitive information such as gender or ethnicity in the feature representation while retaining competitive performance in a face recognition task.

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"
    - "some research problems in biometrics the future beckons"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"
    - "deep learning for face recognition pride or prejudiced"

Connected through hyperedge HE-2 (induced by source paper "privacynet semi adversarial networks for multi attribute face privacy"):
  with members:
    - "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"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

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"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "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:
    - "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"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "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:
    - "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"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-6 (induced by source paper "algorithmic discrimination formulation and exploration in deep learning based face biometrics"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "diversity in faces"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"
    - "discrimination in the age of algorithms"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-7 (induced by source paper "flowsan privacy enhancing semi adversarial networks to confound arbitrary face based gender classifiers"):
  with members:
    - "right for the right reason training agnostic networks"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "a survey to deep facial attribute analysis"
    - "using lip to gloss over faces in single stage face detection networks"

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)