human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "efficient privacy preservation of big data for accurate data mining"
  Abstract: Abstract   Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that potentially sensitive data may become public if the collected data are not appropriately sanitized before being released for investigation. Although there are more than a few privacy-preserving methods available, they are not efficient, scalable, or have problems with data utility, or privacy. This paper addresses these issues by proposing an efficient and scalable nonreversible perturbation algorithm, PABIDOT, for privacy preservation of big data via optimal geometric transformations. PABIDOT was tested for efficiency, scalability, attack resistance, and accuracy using nine datasets and five classification algorithms. Experiments show that PABIDOT excels in execution speed, scalability, attack resistance, and accuracy in large-scale privacy-preserving data classification when compared with two other, related privacy-preserving algorithms.

Connected through hyperedge HE-1 (induced by source paper "local differential privacy for deep learning"):
  with members:
    - "an energy driven network function virtualization for multi domain software defined networks"
    - "an efficient and scalable privacy preserving algorithm for big data and data streams"
    - "mvg mechanism differential privacy under matrix valued query"
    - "efficient data perturbation for privacy preserving and accurate data stream mining"
    - "towards practical differential privacy for sql queries"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "machine learning models that remember too much"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "an efficient and scalable privacy preserving algorithm for big data and data streams"):
  with members:
    - "efficient data perturbation for privacy preserving and accurate data stream mining"
    - "benchmarking distributed stream processing platforms for iot applications"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "building confidential and efficient query services in the cloud with rasp data perturbation"
    - "composition attacks and auxiliary information in data privacy"
    - "a framework for high accuracy privacy preserving mining"

Question: Please tell me which class the center node 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)