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 "efficient and private approximations of distributed databases calculations"):
  Abstract: In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separated databases and, a relative to it, issue of private data release were intensively investigated. However, despite a considerable progress, computational complexity, due to an increasing size of data, remains a limiting factor in real-world deployments, especially in case of privacy-preserving computations. #R##N#In this paper, we present a general method for trade off between performance and accuracy of distributed calculations by performing data sampling. Sampling was a topic of extensive research that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on approximation of intersection set both without and with privacy-preserving mechanism. An analysis of the bound on error as a function of the sample size is discussed and heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach.

Includes members:
  Member "a minimal variance estimator for the cardinality of big data set intersection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a memory efficient sketch method for estimating high similarities in streaming sets")
  Member "parallel correctness and transferability for conjunctive queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a case for stale synchronous distributed model for declarative recursive computation")
  Member "when distributed computation is communication expensive":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the communication complexity of optimization")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "locally private hypothesis selection")
  Member "what you can do with coordinated samples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "count min optimal estimation and tight error bounds using empirical error distributions")
  Member "coordinated weighted sampling for estimating aggregates over multiple weight assignments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self similar epochs value in arrangement")
  Member "explicit learning curves for transduction and application to clustering and compression algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improvability through semi supervised learning a survey of theoretical results")

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.DB(Databases)