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: "distributed event localization via alternating direction method of multipliers"
  Abstract: This paper addresses the problem of distributed event localization using noisy range measurements with respect to sensors with known positions. Event localization is fundamental in many wireless sensor network applications such as homeland security, law enforcement, and environmental studies. However, a distributed algorithm is still lacking which can split the intensive localization computation among a wireless sensor network in which individual sensors only have limited computational capacity. Based on the alternating direction method of multipliers (ADMM), we propose a distributed event localization structure and two scalable distributed algorithms named GS-ADMM and J-ADMM respectively. More specifically, we consider a scenario in which the entire sensor network is divided into several clusters with a cluster head collecting measurements within each cluster and performing local localization. In the meantime, the cluster heads exchange intermediate computation information which will be factored into their local computations to achieve consistency (consensus) across the localization results of all cluster heads. Simulation results confirm that the proposed approaches can indeed achieve localization consistency (consensus) across the clusters and each cluster can obtain better localization performance compared with the case in which cluster heads only use local measurements within clusters.

Connected through hyperedge HE-1 (induced by source paper "recycled admm improve privacy and accuracy with less computation in distributed algorithms"):
  with members:
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "distributed constraint optimization problems and applications a survey"
    - "dp admm admm based distributed learning with differential privacy"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "fast and differentially private algorithms for decentralized collaborative machine learning"
    - "adaptive consensus admm for distributed optimization"
    - "adaptive admm with spectral penalty parameter selection"
    - "differentially private distributed optimization"

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.DC(Distributed, Parallel, and Cluster Computing)