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 "evaluating the utility of anonymized network traces for intrusion detection"):
  Abstract: Anonymization is the process of removing or hiding sensitive information in logs. Anonymization allows organizations to share network logs while not exposing sensitive information. However, there is an inherent trade off between the amount of information revealed in the log and the usefulness of the log to the client (the utility of a log). There are many anonymization techniques, and there are many ways to anonymize a particular log (that is, which fields to anonymize and how). Different anonymization policies will result in logs with varying levels of utility for analysis. In this paper we explore the effect of different anonymization policies on logs. We provide an empirical analysis of the effect of varying anonymization policies by looking at the number of alerts generated by an Intrusion Detection System. This is the first work to thoroughly evaluate the effect of single field anonymization policies on a data set. Our main contributions are to determine a set of fields that have a large impact on the utility of a log.

Includes members:
  Member "how to break anonymity of the netflix prize dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy preserving blockchain based federated learning with differential data sharing")
  Member "scrub pa a multi level multi dimensional anonymization tool for process accounting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward trusted sharing of network packet traces using anonymization single field privacy analysis tradeoffs")
  Member "flaim a multi level anonymization framework for computer and network logs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "preserving both privacy and utility in network trace anonymization")
  Member "sharing computer network logs for security and privacy a motivation for new methodologies of anonymization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "controlled data sharing for collaborative predictive blacklisting")

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.CR(Cryptography and Security)