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 "recycled admm improve privacy and accuracy with less computation in distributed algorithms"):
  Abstract: Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-utility tradeoff. In this study we propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. We obtain a sufficient condition for the convergence of R-ADMM and provide the privacy analysis based on objective perturbation.

Includes members:
  Member "straggler resilient and communication efficient distributed iterative linear solver":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "distributed constraint optimization problems and applications a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hs cai a hybrid dcop algorithm via combining search with context based inference")
  Member "dp admm admm based distributed learning with differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis and optimization in privacy preserving federated learning")
  Member "improving the privacy and accuracy of admm based distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local sgd with periodic averaging tighter analysis and adaptive synchronization")
  Member "fast and differentially private algorithms for decentralized collaborative machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recycled admm improving the privacy and accuracy of distributed algorithms")
  Member "adaptive consensus admm for distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "doublesqueeze parallel stochastic gradient descent with double pass error compensated compression")
  Member "adaptive admm with spectral penalty parameter selection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "catching cheats detecting strategic manipulation in distributed optimisation of electric vehicle aggregators")
  Member "distributed event localization via alternating direction method of multipliers":
    also connected through other hyperedges:
      (none)

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)