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 "an efficient and scalable privacy preserving algorithm for big data and data streams"):
  Abstract: Abstract   A vast amount of valuable data is produced and is becoming available for analysis as a result of advancements in smart cyber-physical systems. The data comes from various sources, such as healthcare, smart homes, smart vehicles, and often includes private, potentially sensitive information that needs appropriate sanitization before being released for analysis. The incremental and fast nature of data generation in these systems necessitates scalable privacy-preserving mechanisms with high privacy and utility. However, privacy preservation often comes at the expense of data utility. We propose a new data perturbation algorithm, SEAL (Secure and Efficient data perturbation Algorithm utilizing Local differential privacy), based on Chebyshev interpolation and Laplacian noise, which provides a good balance between privacy and utility with high efficiency and scalability. Empirical comparisons with existing privacy-preserving algorithms show that SEAL excels in execution speed, scalability, accuracy, and attack resistance. SEAL provides flexibility in choosing the best possible privacy parameters, such as the amount of added noise, which can be tailored to the domain and dataset.

Includes members:
  Member "efficient privacy preservation of big data for accurate data mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "local differential privacy for deep learning")
  Member "efficient data perturbation for privacy preserving and accurate data stream mining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local differential privacy for deep learning")
  Member "benchmarking distributed stream processing platforms for iot applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of benchmarks to evaluate data analytics for smart applications")
  Member "rappor randomized aggregatable privacy preserving ordinal response":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally private hypothesis selection")
  Member "extremal mechanisms for local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "building confidential and efficient query services in the cloud with rasp data perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local differential privacy for deep learning")
  Member "composition attacks and auxiliary information in data privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local differential privacy for deep learning")
  Member "a framework for high accuracy privacy preserving mining":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the price of differential privacy for low rank factorization")

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)