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: "characterizing the variability in face recognition accuracy relative to race"
  Abstract: Many recent news headlines have labeled face recognition technology as biased or racist. We report on a methodical investigation into differences in face recognition accuracy between African-American and Caucasian image cohorts of the MORPH dataset. We find that, for all four matchers considered, the impostor and the genuine distributions are statistically significantly different between cohorts. For a fixed decision threshold, the African-American image cohort has a higher false match rate and a lower false non-match rate. ROC curves compare verification rates at the same false match rate, but the different cohorts achieve the same false match rate at different thresholds. This means that ROC comparisons are not relevant to operational scenarios that use a fixed decision threshold. We show that, for the ResNet matcher, the two cohorts have approximately equal separation of impostor and genuine distributions. Using ICAO compliance as a standard of image quality, we find that the initial image cohorts have unequal rates of good quality images. The ICAO-compliant subsets of the original image cohorts show improved accuracy, with the main effect being to reducing the low-similarity tail of the genuine distributions.

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 "reducing geographic performance differential for face recognition"):
  with members:
    - "accuracy comparison across face recognition algorithms where are we on measuring race bias"
    - "identity document to selfie face matching across adolescence"
    - "deep learning is robust to massive label noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "docface matching id document photos to selfies"
    - "docface id document to selfie matching"
    - "an empirical study of example forgetting during deep neural network learning"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-3 (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"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "probabilistic face embeddings"
    - "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-4 (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"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "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-5 (induced by source paper "four principles of explainable ai as applied to biometrics and facial forensic algorithms"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention mechanism in speaker recognition what does it learn in deep speaker embedding"
    - "generating visual explanations"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-6 (induced by source paper "does face recognition accuracy get better with age deep face matchers say no"):
  with members:
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "accuracy comparison across face recognition algorithms where are we on measuring race bias"):
  with members:
    - "debface de biasing face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deep residual learning for image recognition"

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)