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 "on differentially private filtering for event streams"):
  Abstract: Rigorous privacy mechanisms that can cope with dynamic data are required to encourage a wider adoption of large-scale monitoring and decision systems relying on end-user information. A promising approach to develop these mechanisms is to specify quantitative privacy requirements at design time rather than as an afterthought, and to rely on signal processing techniques to achieve satisfying trade-offs between privacy and performance specifications. This paper discusses, from the signal processing point of view, an event stream analysis problem introduced in the database and cryptography literature. A discrete-valued input signal describes the occurrence of events contributed by end-users, and a system is supposed to provide some output signal based on this information, while preserving the privacy of the participants. The notion of privacy adopted here is that of event-level differential privacy, which provides strong privacy guarantees and has important operational advantages. Several mechanisms are described to provide differentially private output signals while minimizing the impact on performance. These mechanisms demonstrate the benefits of leveraging system theoretic techniques to provide privacy guarantees for dynamic systems.

Includes members:
  Member "private decayed predicate sums on streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "money cannot buy everything trading infinite location data streams with bounded individual privacy loss")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "compressive mechanism utilizing sparse representation in differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")

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)