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: "a voting based system for ethical decision making"
  Abstract: We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice. In a nutshell, we propose to learn a model of societal preferences, and, when faced with a specific ethical dilemma at runtime, efficiently aggregate those preferences to identify a desirable choice. We provide a concrete algorithm that instantiates our approach; some of its crucial steps are informed by a new theory of swap-dominance efficient voting rules. Finally, we implement and evaluate a system for ethical decision making in the autonomous vehicle domain, using preference data collected from 1.3 million people through the Moral Machine website.

Connected through hyperedge HE-1 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"
    - "manipulating and measuring model interpretability"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "on formalizing fairness in prediction with machine learning"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving crowd guided ai decision making in ethical dilemmas"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "deep tractable probabilistic models for moral responsibility"
    - "building ethics into artificial intelligence"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "the social dilemma of autonomous vehicles"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-3 (induced by source paper "a human in the loop framework to construct context dependent mathematical formulations of fairness"):
  with members:
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-4 (induced by source paper "making the cut a bandit based approach to tiered interviewing"):
  with members:
    - "the diverse cohort selection problem"
    - "learning to hire teams"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "multiple identifications in multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "teaching ai ethics law and policy"):
  with members:
    - "on a formal model of safe and scalable self driving cars"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "fairness through awareness"

Connected through hyperedge HE-6 (induced by source paper "grounding value alignment with ethical principles"):
  with members:
    - "a computational model of commonsense moral decision making"
    - "research priorities for robust and beneficial artificial intelligence"

Connected through hyperedge HE-7 (induced by source paper "a roadmap for robust end to end alignment"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "fsgan subject agnostic face swapping and reenactment"
    - "one pixel attack for fooling deep neural networks"
    - "a survey on bias and fairness in machine learning"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "sgd decentralized byzantine resilience"
    - "recent advances in algorithmic high dimensional robust statistics"
    - "reward tampering problems and solutions in reinforcement learning a causal influence diagram perspective"

Connected through hyperedge HE-8 (induced by source paper "envy free classification"):
  with members:
    - "probably approximately metric fair learning"
    - "learning non discriminatory predictors"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "discrimination in online ad delivery"
    - "multiclass learning approaches a theoretical comparison with implications"
    - "fairness through awareness"

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.AI(Artificial Intelligence)