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: "an experimental evaluation of covariates effects on unconstrained face verification"
  Abstract: Covariates are factors that have a debilitating influence on face verification performance. In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance. To study the first problem, we implement five state-of-the-art deep convolutional networks and evaluate them on three challenging covariates datasets. In total, seven covariates are considered: pose (yaw and roll), age, facial hair, gender, indoor/outdoor, occlusion (nose and mouth visibility, and forehead visibility), and skin tone. We first report the performance of each individual network on the overall protocol and use the score-level fusion method to analyze each covariate. Some of the results confirm and extend the findings of previous studies, and others are new findings that were rarely mentioned previously or did not show consistent trends. For the second problem, we demonstrate that with the assistance of gender information, the quality of a precurated noisy large-scale face dataset for face recognition can be further improved. After retraining the face recognition model using the curated data, performance improvement is observed at low false acceptance rates.

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"
    - "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"
    - "deep learning for face recognition pride or prejudiced"

Connected through hyperedge HE-2 (induced by source paper "how does gender balance in training data affect face recognition accuracy"):
  with members:
    - "analysis of gender inequality in face recognition accuracy"
    - "does face recognition accuracy get better with age deep face matchers say no"
    - "probabilistic face embeddings"
    - "characterizing the variability in face recognition accuracy relative to race"
    - "retinaface single stage dense face localisation in the wild"
    - "the devil of face recognition is in the noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "analysis of gender inequality in face recognition accuracy"):
  with members:
    - "does face recognition accuracy get better with age deep face matchers say no"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "characterizing the variability in face recognition accuracy relative to race"
    - "retinaface single stage dense face localisation in the wild"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "demographic bias in presentation attack detection of iris recognition systems"):
  with members:
    - "biometric face presentation attack detection with multi channel convolutional neural network"
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "unsupervised domain specific deblurring via disentangled representations"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "exploring disentangled feature representation beyond face identification"
    - "towards open set identity preserving face synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep semantic face deblurring"
    - "drit diverse image to image translation via disentangled representations"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "deep face recognition model compression via knowledge transfer and distillation"):
  with members:
    - "p2sgrad refined gradients for optimizing deep face models"
    - "retinaface single stage dense face localisation in the wild"
    - "on measuring the iconicity of a face"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "l2 constrained softmax loss for discriminative face verification"
    - "systematic evaluation of convolution neural network advances on the imagenet"

Connected through hyperedge HE-7 (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"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "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-8 (induced by source paper "does face recognition accuracy get better with age deep face matchers say no"):
  with members:
    - "characterizing the variability in face recognition accuracy relative to race"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "the megaface benchmark 1 million faces for recognition at scale"

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)