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 "gdpr anti patterns how design and operation of modern cloud scale systems conflict with gdpr"):
  Abstract: In recent years, our society is being plagued by unprecedented levels of privacy and security breaches. To rein in this trend, the European Union, in 2018, introduced a comprehensive legislation called the General Data Protection Regulation (GDPR). In this article, we review GDPR from a systems perspective, and identify how the design and operation of modern cloud-scale systems conflict with this regulation. We illustrate these conflicts via six GDPR anti-patterns: storing data without a clear timeline for deletion; reusing data indiscriminately; creating walled gardens and black markets; risk-agnostic data processing; hiding data breaches; making unexplainable decisions. Our findings reveal deep-rooted tussle between GDPR requirements and how cloud-scale systems that process personal data have evolved in the modern era. While it is imperative to avoid these anti-patterns, we believe that achieving compliance requires comprehensive, grounds up solutions; anything short would amount to fixing a leaky faucet in a sinking ship.

Includes members:
  Member "un informed consent studying gdpr consent notices in the field":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond the front page measuring third party dynamics in the field")
  Member "the seven sins of personal data processing systems under gdpr":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding and benchmarking the impact of gdpr on database systems")
  Member "counterfactual explanations without opening the black box automated decisions and the gdpr":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")

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)