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: "privacy issues and data protection in big data a case study analysis under gdpr"
  Abstract: Big data has become a great asset for many organizations, promising improved operations and new business opportunities. However, big data has increased access to sensitive information that when processed can directly jeopardize the privacy of individuals and violate data protection laws. As a consequence, data controllers and data processors may be imposed tough penalties for non-compliance that can result even to bankruptcy. In this paper, we discuss the current state of the legal regulations and analyze different data protection and privacy-preserving techniques in the context of big data analysis. In addition, we present and analyze two real-life research projects as case studies dealing with sensitive data and actions for complying with the data regulation laws. We show which types of information might become a privacy risk, the employed privacy-preserving techniques in accordance with the legal requirements, and the influence of these techniques on the data processing phase and the research results.

Connected through hyperedge HE-1 (induced by source paper "secondary use of electronic health record opportunities and challenges"):
  with members:
    - "can autism be catered with artificial intelligence assisted intervention technology a comprehensive survey"
    - "a novel database of children s spontaneous facial expressions liris cse"
    - "are we there yet understanding the challenges faced in complying with the general data protection regulation gdpr"

Connected through hyperedge HE-2 (induced by source paper "data capsule a new paradigm for automatic compliance with data privacy regulations"):
  with members:
    - "how to make privacy policies both gdpr compliant and usable"

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.CR(Cryptography and Security)