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: "predictive inequity in object detection"
  Abstract: In this work, we investigate whether state-of-the-art object detection systems have equitable predictive performance on pedestrians with different skin tones. This work is motivated by many recent examples of ML and vision systems displaying higher error rates for certain demographic groups than others. We annotate an existing large scale dataset which contains pedestrians, BDD100K, with Fitzpatrick skin tones in ranges [1-3] or [4-6]. We then provide an in-depth comparative analysis of performance between these two skin tone groupings, finding that neither time of day nor occlusion explain this behavior, suggesting this disparity is not merely the result of pedestrians in the 4-6 range appearing in more difficult scenes for detection. We investigate to what extent time of day, occlusion, and reweighting the supervised loss during training affect this predictive bias.

Connected through hyperedge HE-1 (induced by source paper "machines learn appearance bias in face recognition"):
  with members:
    - "saving face investigating the ethical concerns of facial recognition auditing"
    - "explicit bias discovery in visual question answering models"
    - "women also snowboard overcoming bias in captioning models"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "characterizing bias in classifiers using generative models"):
  with members:
    - "identifying bias in ai using simulation"
    - "progressive growing of gans for improved quality stability and variation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "learning face age progression a pyramid architecture of gans"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "unsupervised image to image translation with generative adversarial networks"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "detecting bias with generative counterfactual face attribute augmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model cards for model reporting"
    - "women also snowboard overcoming bias in captioning models"
    - "counterfactual fairness in text classification through robustness"
    - "explaining explanations an overview of interpretability of machine learning"
    - "explaining image classifiers by counterfactual generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-4 (induced by source paper "fairyted a fair rating predictor for ted talk data"):
  with members:
    - "transfer of machine learning fairness across domains"
    - "a causality guided prediction of the ted talk ratings from the speech transcripts using neural networks"
    - "a survey on bias and fairness in machine learning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rawlsian fairness for machine learning"
    - "automatic text scoring using neural networks"

Connected through hyperedge HE-5 (induced by source paper "fairvis visual analytics for discovering intersectional bias in machine learning"):
  with members:
    - "putting fairness principles into practice challenges metrics and improvements"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "fairgan fairness aware generative adversarial networks"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-6 (induced by source paper "estimating skin tone and effects on classification performance in dermatology datasets"):
  with members:
    - "diversity in faces"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "mask r cnn"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"

Connected through hyperedge HE-7 (induced by source paper "algorithmic injustices towards a relational ethics"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-8 (induced by source paper "making ethical decisions for the immersive web"):
  with members:
    - "systematic classification of side channel attacks a case study for mobile devices"

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)