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 parameter estimation in sensor networks nonlinear observation models and imperfect communication"):
  Abstract: The paper studies distributed static parameter (vector) estimation in sensor networks with nonlinear observation models and noisy inter-sensor communication. It introduces \emph{separably estimable} observation models that generalize the observability condition in linear centralized estimation to nonlinear distributed estimation. It studies two distributed estimation algorithms in separably estimable models, the $\mathcal{NU}$ (with its linear counterpart $\mathcal{LU}$) and the $\mathcal{NLU}$. Their update rule combines a \emph{consensus} step (where each sensor updates the state by weight averaging it with its neighbors' states) and an \emph{innovation} step (where each sensor processes its local current observation.) This makes the three algorithms of the \textit{consensus + innovations} type, very different from traditional consensus. The paper proves consistency (all sensors reach consensus almost surely and converge to the true parameter value,) efficiency, and asymptotic unbiasedness. For $\mathcal{LU}$ and $\mathcal{NU}$, it proves asymptotic normality and provides convergence rate guarantees. The three algorithms are characterized by appropriately chosen decaying weight sequences. Algorithms $\mathcal{LU}$ and $\mathcal{NU}$ are analyzed in the framework of stochastic approximation theory; algorithm $\mathcal{NLU}$ exhibits mixed time-scale behavior and biased perturbations, and its analysis requires a different approach that is developed in the paper.

Includes members:
  Member "distributed detection over noisy networks large deviations analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed learning with adversarial agents under relaxed network condition")
  Member "gossip and distributed kalman filtering weak consensus under weak detectability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sample greedy gossip for distributed network wide average computation")
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "supervised learning under distributed features")
  Member "weight optimization for consensus algorithms with correlated switching topology":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online resource inference in network utility maximization problems")
  Member "distributed and recursive parameter estimation in parametrized linear state space models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explicit convergence rate of a distributed alternating direction method of multipliers")
  Member "sensor networks with random links topology design for distributed consensus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matcha speeding up decentralized sgd via matching decomposition sampling")
  Member "distributing the kalman filter for large scale systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coded distributed tracking")
  Member "distributed consensus algorithms in sensor networks quantized data and random link failures":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fully decentralized multi agent reinforcement learning with networked agents")

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.MA(Multiagent Systems)