human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity"):
  Abstract: The emergence and growth of research on issues of ethics in AI, and in particular algorithmic fairness, has roots in an essential observation that structural inequalities in society are reflected in the data used to train predictive models and in the design of objective functions. While research aiming to mitigate these issues is inherently interdisciplinary, the design of unbiased algorithms and fair socio-technical systems are key desired outcomes which depend on practitioners from the fields of data science and computing. However, these computing fields broadly also suffer from the same under-representation issues that are found in the datasets we analyze. This disconnect affects the design of both the desired outcomes and metrics by which we measure success. If the ethical AI research community accepts this, we tacitly endorse the status quo and contradict the goals of non-discrimination and equity which work on algorithmic fairness, accountability, and transparency seeks to address. Therefore, we advocate in this work for diversifying computing as a core priority of the field and our efforts to achieve ethical AI practices. We draw connections between the lack of diversity within academic and professional computing fields and the type and breadth of the biases encountered in datasets, machine learning models, problem formulations, and interpretation of results. Examining the current fairness/ethics in AI literature, we highlight cases where this lack of diverse perspectives has been foundational to the inequity in treatment of underrepresented and protected group data. We also look to other professional communities, such as in law and health, where disparities have been reduced both in the educational diversity of trainees and among professional practices. We use these lessons to develop recommendations that provide concrete steps for the computing community to increase diversity.

Includes members:
  Member "problem formulation and fairness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding artificial intelligence ethics and safety")
  Member "efficient search for diverse coherent explanations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans")
  Member "improving fairness in machine learning systems what do industry practitioners need":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "crowdsourcing with fairness diversity and budget constraints":
    also connected through other hyperedges:
      (none)
  Member "the social cost of strategic classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "decisions counterfactual explanations and strategic behavior")
  Member "fairness through causal awareness learning latent variable models for biased data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "counterfactual reasoning for fair clinical risk prediction")
  Member "creating fair models of atherosclerotic cardiovascular disease risk":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "counterfactual reasoning for fair clinical risk prediction")
  Member "a moral framework for understanding of fair ml through economic models of equality of opportunity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning")

Question: Please tell me which class the source paper 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)