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 "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.

Includes members:
  Member "distributed maximum likelihood sensor network localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "peregrine network localization and navigation with scalable inference and efficient operation")
  Member "networked mimo with clustered linear precoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")

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.DC(Distributed, Parallel, and Cluster Computing)