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 "k epsilon anonymity k anonymity with epsilon differential privacy"):
  Abstract: The explosion in volume and variety of data offers enormous potential for research and commercial use. Increased availability of personal data is of particular interest in enabling highly customised services tuned to individual needs. Preserving the privacy of individuals against reidentification attacks in this fast-moving ecosystem poses significant challenges for a one-size fits all approach to anonymisation. #R##N#In this paper we present ($k$,$\epsilon$)-anonymisation, an approach that combines the $k$-anonymisation and $\epsilon$-differential privacy models into a single coherent framework, providing privacy guarantees at least as strong as those offered by the individual models. Linking risks of less than 5\% are observed in experimental results, even with modest values of $k$ and $\epsilon$. #R##N#Our approach is shown to address well-known limitations of $k$-anonymity and $\epsilon$-differential privacy and is validated in an extensive experimental campaign using openly available datasets.

Includes members:
  Member "optimal differentially private mechanisms for randomised response":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private rank aggregation under local differential privacy")
  Member "differentially private response mechanisms on categorical data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal differentially private mechanisms for randomised response")
  Member "from t closeness to differential privacy and vice versa in data anonymization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "techniques d anonymisation tabulaire concepts et mise en oeuvre")
  Member "on sampling anonymization and differential privacy or k anonymization meets differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "data dependent differentially private parameter learning for directed graphical models")

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)