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: "responsible ai by design"
  Abstract: Recently, a lot of attention has been given to undesired consequences of Artificial Intelligence (AI), such as unfair bias leading to discrimination, or the lack of explanations of the results of AI systems. There are several important questions to answer before AI can be deployed at scale in our businesses and societies. Most of these issues are being discussed by experts and the wider communities, and it seems there is broad consensus on where they come from. There is, however, less consensus on, and experience with how to practically deal with those issues in organizations that develop and use AI, both from a technical and organizational perspective. In this paper, we discuss the practical case of a large organization that is putting in place a company-wide methodology to minimize the risk of undesired consequences of AI. We hope that other organizations can learn from this and that our experience contributes to making the best of AI while minimizing its risks.

Connected through hyperedge HE-1 (induced by source paper "rule extraction in unsupervised anomaly detection for model explainability application to oneclass svm"):
  with members:
    - "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "aequitas a bias and fairness audit toolkit"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "explaining explanations an overview of interpretability of machine learning"
    - "equality of opportunity in supervised learning"

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)