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 "anonymization with worst case distribution based background knowledge"):
  Abstract: Background knowledge is an important factor in privacy preserving data publishing. Distribution-based background knowledge is one of the well studied background knowledge. However, to the best of our knowledge, there is no existing work considering the distribution-based background knowledge in the worst case scenario, by which we mean that the adversary has accurate knowledge about the distribution of sensitive values according to some tuple attributes. Considering this worst case scenario is essential because we cannot overlook any breaching possibility. In this paper, we propose an algorithm to anonymize dataset in order to protect individual privacy by considering this background knowledge. We prove that the anonymized datasets generated by our proposed algorithm protects individual privacy. Our empirical studies show that our method preserves high utility for the published data at the same time.

Includes members:
  Member "composition attacks and auxiliary information in data privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "local differential privacy for deep learning")
  Member "worst case background knowledge for privacy preserving data publishing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "kloakdb a platform for analyzing sensitive data with k anonymous query processing")

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.DB(Databases)