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 convergence verification for gaussian belief propagation"):
  Abstract: Gaussian belief propagation (BP) is a computationally efficient method to approximate the marginal distribution and has been widely used for inference with high dimensional data as well as distributed estimation in large-scale networks. However, the convergence of Gaussian BP is still an open issue. Though sufficient convergence conditions have been studied in the literature, verifying these conditions requires gathering all the information over the whole network, which defeats the main advantage of distributed computing by using Gaussian BP. In this paper, we propose a novel sufficient convergence condition for Gaussian BP that applies to both the pairwise linear Gaussian model and to Gaussian Markov random fields. We show analytically that this sufficient convergence condition can be easily verified in a distributed way that satisfies the network topology constraint.

Includes members:
  Member "convergence analysis of the information matrix in gaussian belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast resampling of three dimensional point clouds via graphs")
  Member "convergence analysis of belief propagation for pairwise linear gaussian models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed multi user wireless charging power allocation")
  Member "distributed hybrid power state estimation under pmu sampling phase errors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rf based energy harvesting in decode and forward relaying systems ergodic and outage capacities")
  Member "distributed clock skew and offset estimation in wireless sensor networks asynchronous algorithm and convergence analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperative network synchronization asymptotic analysis")
  Member "low complexity detection in large dimension mimo isi channels using graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deterministic sampling decoding where sphere decoding meets lattice gaussian distribution")
  Member "gaussian belief propagation solver for systems of linear equations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convergence analysis of belief propagation for pairwise linear gaussian models")

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.IT(Information Theory)