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: "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
  Abstract: Recent research has proposed the use of Semi Adversarial Networks (SAN) for imparting privacy to face images. SANs are convolutional autoencoders that perturb face images such that the perturbed images cannot be reliably used by an attribute classifier (e.g., a gender classifier) but can still be used by a face matcher for matching purposes. However, the generalizability of SANs across multiple arbitrary gender classifiers has not been demonstrated in the literature. In this work, we tackle the generalization issue by designing an ensemble SAN model that generates a diverse set of perturbed outputs for a given input face image. This is accomplished by enforcing diversity among the individual models in the ensemble through the use of different data augmentation techniques. The goal is to ensure that at least one of the perturbed output faces will confound an arbitrary, previously unseen gender classifier. Extensive experiments using different unseen gender classifiers and face matchers are performed to demonstrate the efficacy of the proposed paradigm in imparting gender privacy to face images.

Connected through hyperedge HE-1 (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"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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-4 (induced by source paper "towards large yet imperceptible adversarial image perturbations with perceptual color distance"):
  with members:
    - "perceptual quality preserving black box attack against deep learning image classifiers"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "smooth adversarial examples"
    - "a general framework for adversarial examples with objectives"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "towards imperceptible and robust adversarial example attacks against neural networks"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive overview of biometric fusion"):
  with members:
    - "attribute centered loss for soft biometrics guided face sketch photo recognition"
    - "anonymizing k facial attributes via adversarial perturbations"
    - "unravelling robustness of deep learning based face recognition against adversarial attacks"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "class representative autoencoder for low resolution multi spectral gender classification"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "review of the fingerprint liveness detection livdet competition series 2009 to 2015"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "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-8 (induced by source paper "some research problems in biometrics the future beckons"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "iris recognition after death"

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)