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: "linear convergence rate of a class of distributed augmented lagrangian algorithms"
  Abstract: We study distributed optimization where nodes cooperatively minimize the sum of their individual, locally known, convex costs $f_i(x)$'s, $x \in {\mathbb R}^d$ is global. Distributed augmented Lagrangian (AL) methods have good empirical performance on several signal processing and learning applications, but there is limited understanding of their convergence rates and how it depends on the underlying network. This paper establishes globally linear (geometric) convergence rates of a class of deterministic and randomized distributed AL methods, when the $f_i$'s are twice continuously differentiable and have a bounded Hessian. We give explicit dependence of the convergence rates on the underlying network parameters. Simulations illustrate our analytical findings.

Connected through hyperedge HE-1 (induced by source paper "cluster based distributed augmented lagrangian algorithm for a class of constrained convex optimization problems"):
  with members:
    - "convergence analysis of saddle point problems in time varying wireless systems control theoretical approach"
    - "zero gradient sum algorithms for distributed convex optimization the continuous time case"

Connected through hyperedge HE-2 (induced by source paper "djam distributed jacobi asynchronous method for learning personal models"):
  with members:
    - "personalized and private peer to peer machine learning"
    - "decentralized collaborative learning of personalized models over networks"
    - "network lasso clustering and optimization in large graphs"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "a unification and generalization of exact distributed first order methods"):
  with members:
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "convergence rates of distributed nesterov like gradient methods on random networks"
    - "fast distributed gradient methods"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-4 (induced by source paper "control interpretations for first order optimization methods"):
  with members:
    - "exponential convergence bounds using integral quadratic constraints"

Connected through hyperedge HE-5 (induced by source paper "asynchronous distributed admm for large scale optimization part ii linear convergence analysis and numerical performance"):
  with members:
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"
    - "multi agent distributed optimization via inexact consensus admm"
    - "decomposition by partial linearization parallel optimization of multi agent systems"

Connected through hyperedge HE-6 (induced by source paper "multi agent distributed optimization via inexact consensus admm"):
  with members:
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-7 (induced by source paper "explicit convergence rate of a distributed alternating direction method of multipliers"):
  with members:
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "a reliable effective terascale linear learning system"
    - "distributed and recursive parameter estimation in parametrized linear state space models"

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.IT(Information Theory)