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 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.

Connected through hyperedge HE-1 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond"):
  with members:
    - "distributed learning in non convex environments part ii polynomial escape from saddle points"
    - "decentralized stochastic gradient tracking for empirical risk minimization"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "stochastic gradient push for distributed deep learning"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-2 (induced by source paper "clustering with distributed data"):
  with members:
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "adding vs averaging in distributed primal dual optimization"
    - "distributed k means algorithm"
    - "distributed k means and k median clustering on general topologies"
    - "distributed strongly convex optimization"
    - "scalable k means"
    - "fast distributed gradient methods"

Connected through hyperedge HE-3 (induced by source paper "cyber social systems modeling inference and optimal design"):
  with members:
    - "structural cost optimal design of sensor networks for distributed estimation"
    - "distributed estimation recovery under sensor failure"
    - "observational equivalence in system estimation contractions in complex networks"
    - "active sensing of social networks"
    - "measurement partitioning and observational equivalence in state estimation"
    - "graph theoretic distributed inference in social networks"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-4 (induced by source paper "non bayesian social learning with uncertain models"):
  with members:
    - "social learning over weakly connected graphs"
    - "defending non bayesian learning against adversarial attacks"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"

Connected through hyperedge HE-5 (induced by source paper "distributed parameter estimation under event triggered communications"):
  with members:
    - "distributed kalman filters with state equality constraints time based and event triggered communications"
    - "stochastic event triggered sensor scheduling for remote state estimation"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"

Connected through hyperedge HE-6 (induced by source paper "coordinate descent diffusion learning by networked agents"):
  with members:
    - "asynchronous adaptation and learning over networks part iii comparison analysis"
    - "asynchronous adaptation and learning over networks part ii performance analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "distributing the kalman filter for large scale systems"

Connected through hyperedge HE-7 (induced by source paper "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"):
  with members:
    - "byzantine resilient distributed observers for lti systems"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "byzantine vector consensus in complete graphs"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-8 (induced by source paper "distributed learning of average belief over networks using sequential observations"):
  with members:
    - "dynamic power distribution system management with a locally connected communication network"
    - "learning and management for internet of things accounting for adaptivity and scalability"
    - "distributed equilibrium learning for power network voltage control with a locally connected communication network"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

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.MA(Multiagent Systems)