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: "big data data science and civil rights"
  Abstract: Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how colleges and universities make admissions and financial aid decisions, and much more. As data-driven decisions increasingly affect every corner of our lives, there is an urgent need to ensure they do not become instruments of discrimination, barriers to equality, threats to social justice, and sources of unfairness. In this paper, we argue for a concrete research agenda aimed at addressing these concerns, comprising five areas of emphasis: (i) Determining if models and modeling procedures exhibit objectionable bias; (ii) Building awareness of fairness into machine learning methods; (iii) Improving the transparency and control of data- and model-driven decision making; (iv) Looking beyond the algorithm(s) for sources of bias and unfairness-in the myriad human decisions made during the problem formulation and modeling process; and (v) Supporting the cross-disciplinary scholarship necessary to do all of that well.

Connected through hyperedge HE-1 (induced by source paper "towards fairer datasets filtering and balancing the distribution of the people subtree in the imagenet hierarchy"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "does object recognition work for everyone"
    - "model cards for model reporting"
    - "implicit diversity in image summarization"
    - "large scale long tailed recognition in an open world"
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "fairness in algorithmic decision making an excursion through the lens of causality"):
  with members:
    - "fairness under composition"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "causal reasoning for algorithmic fairness"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "exposing the probabilistic causal structure of discrimination"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "a causal framework for discovering and removing direct and indirect discrimination"

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)