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: "building confidential and efficient query services in the cloud with rasp data perturbation"
  Abstract: With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.

Connected through hyperedge HE-1 (induced by source paper "local differential privacy for deep learning"):
  with members:
    - "efficient privacy preservation of big data for accurate data mining"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving distributed machine learning via local randomization and admm perturbation"):
  with members:
    - "optimal differentially private admm for distributed machine learning"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "robust decentralized learning using admm with unreliable agents"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "distributed representations of sentences and documents"
    - "collaborative deep learning for recommender systems"
    - "differentially private iterative synchronous consensus"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "an efficient and scalable privacy preserving algorithm for big data and data streams"):
  with members:
    - "efficient privacy preservation of big data for accurate data mining"
    - "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"
    - "composition attacks and auxiliary information in data privacy"
    - "a framework for high accuracy privacy preserving mining"

Connected through hyperedge HE-4 (induced by source paper "toward practical privacy preserving analytics for iot and cloud based healthcare systems"):
  with members:
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-5 (induced by source paper "on the use of homomorphic encryption to secure cloud computing services and routing protocols"):
  with members:
    - "a secure database system using homomorphic encryption schemes"
    - "using in memory encrypted databases on the cloud"
    - "secure execution of distributed session programs"
    - "a brief history of the internet"

Connected through hyperedge HE-6 (induced by source paper "recent developments in cloud based systems state of art"):
  with members:
    - "performance measurement of cloud computing services"
    - "piql success tolerant query processing in the cloud"
    - "cloud migration a case study of migrating an enterprise it system to iaas"

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)