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: "discrimination in the age of algorithms"
  Abstract: The law forbids discrimination. But the ambiguity of human decision-making often makes it extraordinarily hard for the legal system to know whether anyone has actually discriminated. To understand how algorithms affect discrimination, we must therefore also understand how they affect the problem of detecting discrimination. By one measure, algorithms are fundamentally opaque, not just cognitively but even mathematically. Yet for the task of proving discrimination, processes involving algorithms can provide crucial forms of transparency that are otherwise unavailable. These benefits do not happen automatically. But with appropriate requirements in place, the use of algorithms will make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred. By forcing a new level of specificity, the use of algorithms also highlights, and makes transparent, central tradeoffs among competing values. Algorithms are not only a threat to be regulated; with the right safeguards in place, they have the potential to be a positive force for equity.

Connected through hyperedge HE-1 (induced by source paper "pretrained ai models performativity mobility and change"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "on measuring social biases in sentence encoders"
    - "spanbert improving pre training by representing and predicting spans"
    - "patentbert patent classification with fine tuning a pre trained bert model"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "end to end open domain question answering with"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "predictive biases in natural language processing models a conceptual framework and overview"):
  with members:
    - "what s in a name reducing bias in bios without access to protected attributes"
    - "evaluating gender bias in machine translation"
    - "model cards for model reporting"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "a framework for understanding unintended consequences of machine learning"
    - "measuring bias in contextualized word representations"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"

Connected through hyperedge HE-3 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "equity of attention amortizing individual fairness in rankings"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "fair clustering through fairlets"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "selection problems in the presence of implicit bias"
    - "a survey on neural network based summarization methods"
    - "text summarization techniques a brief survey"

Connected through hyperedge HE-4 (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"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"
    - "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-5 (induced by source paper "mitigating bias in algorithmic hiring evaluating claims and practices"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "what does it mean to solve the problem of discrimination in hiring social technical and legal perspectives from the uk on automated hiring systems"
    - "50 years of test un fairness lessons for machine learning"
    - "toward fairness in ai for people with disabilities a research roadmap"
    - "problem formulation and fairness"
    - "learning adversarially fair and transferable representations"
    - "ranking with fairness constraints"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-6 (induced by source paper "bias in bias out evaluating the folk wisdom"):
  with members:
    - "algorithmic fairness"
    - "downstream effects of affirmative action"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "learning under selective labels in the presence of expert consistency"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "proxy non discrimination in data driven systems"
    - "inherent trade offs in the fair determination of risk scores"
    - "fairness through awareness"

Connected through hyperedge HE-7 (induced by source paper "what you see is what you get the impact of representation criteria on human bias in hiring"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "ranking with fairness constraints"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

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.CY(Computers and Society)