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: "deep learning for face recognition pride or prejudiced"
  Abstract: Do very high accuracies of deep networks suggest pride of effective AI or are deep networks prejudiced? Do they suffer from in-group biases (own-race-bias and own-age-bias), and mimic the human behavior? Is in-group specific information being encoded sub-consciously by the deep networks? #R##N#This research attempts to answer these questions and presents an in-depth analysis of `bias' in deep learning based face recognition systems. This is the first work which decodes if and where bias is encoded for face recognition. Taking cues from cognitive studies, we inspect if deep networks are also affected by social in- and out-group effect. Networks are analyzed for own-race and own-age bias, both of which have been well established in human beings. The sub-conscious behavior of face recognition models is examined to understand if they encode race or age specific features for face recognition. Analysis is performed based on 36 experiments conducted on multiple datasets. Four deep learning networks either trained from scratch or pre-trained on over 10M images are used. Variations across class activation maps and feature visualizations provide novel insights into the functioning of deep learning systems, suggesting behavior similar to humans. It is our belief that a better understanding of state-of-the-art deep learning networks would enable researchers to address the given challenge of bias in AI, and develop fairer systems.

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 "on the robustness of face recognition algorithms against attacks and bias"):
  with members:
    - "fsgan subject agnostic face swapping and reenactment"
    - "adversarial examples are a natural consequence of test error in noise"
    - "on detecting gans and retouching based synthetic alterations"
    - "on evaluating adversarial robustness"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "fast geometrically perturbed adversarial faces"
    - "recognizing disguised faces in the wild"

Connected through hyperedge HE-3 (induced by source paper "saving face investigating the ethical concerns of facial recognition auditing"):
  with members:
    - "diversity in faces"
    - "model cards for model reporting"
    - "racial categories in machine learning"
    - "oxford handbook on ai ethics book chapter on race and gender"
    - "datasheets for datasets"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-4 (induced by source paper "fairness in deep learning a computational perspective"):
  with members:
    - "learning credible deep neural networks with rationale regularization"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "incorporating priors with feature attribution on text classification"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "compositional fairness constraints for graph embeddings"
    - "flexibly fair representation learning by disentanglement"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-5 (induced by source paper "how are attributes expressed in face dcnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "towards interpretable face recognition"
    - "mine mutual information neural estimation"
    - "opening the black box of deep neural networks via information"
    - "an all in one convolutional neural network for face analysis"

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)