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 "the tyranny of data the bright and dark sides of data driven decision making for social good"):
  Abstract: The unprecedented availability of large-scale human behavioral data is profoundly changing the world we live in. Researchers, companies, governments, financial institutions, non-governmental organizations and also citizen groups are actively experimenting, innovating and adapting algorithmic decision-making tools to understand global patterns of human behavior and provide decision support to tackle problems of societal importance. In this chapter, we focus our attention on social good decision-making algorithms, that is algorithms strongly influencing decision-making and resource optimization of public goods, such as public health, safety, access to finance and fair employment. Through an analysis of specific use cases and approaches, we highlight both the positive opportunities that are created through data-driven algorithmic decision-making, and the potential negative consequences that practitioners should be aware of and address in order to truly realize the potential of this emergent field. We elaborate on the need for these algorithms to provide transparency and accountability, preserve privacy and be tested and evaluated in context, by means of living lab approaches involving citizens. Finally, we turn to the requirements which would make it possible to leverage the predictive power of data-driven human behavior analysis while ensuring transparency, accountability, and civic participation.

Includes members:
  Member "why should i trust you explaining the predictions of any classifier":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "measuring urban deprivation from user generated content":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting population activity dynamics to predict urban epidemiological incidence")
  Member "smartphones in mental health detecting depressive and manic episodes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of attention management systems in ubiquitous computing environments")
  Member "flooding through the lens of mobile phone activity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tackling climate change with machine learning")
  Member "once upon a crime towards crime prediction from demographics and mobile data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "forecasting crime using arima model")
  Member "daily stress recognition from mobile phone data weather conditions and individual traits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heterogeneous network approach to predict individuals mental health")
  Member "money walks a human centric study on the economics of personal mobile data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "no more chasing waterfalls a measurement study of the header bidding ad ecosystem")
  Member "discrimination in online ad delivery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "getting fairness right towards a toolbox for practitioners")

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)