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: "weight optimization for consensus algorithms with correlated switching topology"
  Abstract: We design the weights in consensus algorithms for spatially correlated random topologies. These arise with 1) networks with spatially correlated random link failures and 2) networks with randomized averaging protocols. We show that the weight optimization problem is convex for both symmetric and asymmetric random graphs. With symmetric random networks, we choose the consensus mean-square error (MSE) convergence rate as the optimization criterion and explicitly express this rate as a function of the link formation probabilities, the link formation spatial correlations, and the consensus weights. We prove that the MSE convergence rate is a convex, nonsmooth function of the weights, enabling global optimization of the weights for arbitrary link formation probabilities and link correlation structures. We extend our results to the case of asymmetric random links. We adopt as optimization criterion the mean-square deviation (MSdev) of the nodes' states from the current average state. We prove that MSdev is a convex function of the weights. Simulations show that significant performance gain is achieved with our weight design method when compared with other methods available in the literature.

Connected through hyperedge HE-1 (induced by source paper "online resource inference in network utility maximization problems"):
  with members:
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-2 (induced by source paper "diffusion adaptation over multi agent networks with wireless link impairments"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributing the kalman filter for large scale systems"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-3 (induced by source paper "quantized consensus by the admm probabilistic versus deterministic quantizers"):
  with members:
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-4 (induced by source paper "multitask diffusion adaptation over asynchronous networks"):
  with members:
    - "diffusion lms over multitask networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "distributed diffusion based lms for node specific parameter estimation over adaptive networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "asynchronous adaptation and learning over networks part iii comparison analysis"
    - "asynchronous adaptation and learning over networks part ii performance analysis"

Connected through hyperedge HE-5 (induced by source paper "asynchronous adaptation and learning over networks part i modeling and stability analysis"):
  with members:
    - "distributed pareto optimization via diffusion strategies"
    - "asynchronous adaptation and learning over networks part iii comparison analysis"
    - "asynchronous adaptation and learning over networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"

Connected through hyperedge HE-6 (induced by source paper "convergence rates of distributed nesterov like gradient methods on random networks"):
  with members:
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "a fast distributed proximal gradient method"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "fast distributed gradient methods"
    - "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-7 (induced by source paper "asynchronous adaptation and learning over networks part iii comparison analysis"):
  with members:
    - "asynchronous adaptation and learning over networks part ii performance analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "diffusion adaptation over networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "sensor networks with random links topology design for distributed consensus"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-8 (induced by source paper "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"):
  with members:
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"
    - "convergence time analysis of quantized gossip consensus on digraphs"

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)