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 estimation from relative measurements of heterogeneous and uncertain quality"):
  Abstract: This paper studies the problem of estimation from relative measurements in a graph, where a vector indexed over the nodes has to be reconstructed from relative pairwise measurements of differences between the values of nodes connected by an edge. In order to model the heterogeneity and uncertainty of the measurements, we assume that measurements are affected by additive noise distributed according to a Gaussian mixture. In this original setup, we formulate the problem of computing the Maximum-Likelihood (ML) estimates and we propose two novel Expectation-Maximization (EM) algorithms for its solution. The main difference between the two algorithm is that one of them is distributed and the other is not. The former algorithm is said to be distributed because it allows each node to compute the estimate of its own value by using only information that is directly available at the node itself or from its immediate neighbors. We prove convergence of both algorithms and present numerical simulations to evaluate and compare their performance.

Includes members:
  Member "ergodic randomized algorithms and dynamics over networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "opinion dynamics on correlated subjects in social networks")
  Member "limited benefit of cooperation in distributed relative localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ergodic randomized algorithms and dynamics over networks")
  Member "a distributed classification estimation algorithm for sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed learning from interactions in social networks")
  Member "error scaling laws for linear optimal estimation from relative measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ergodic randomized algorithms and dynamics over networks")

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.SY(Systems and Control)