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 consensus algorithms in sensor networks link failures and channel noise"
  Abstract: The paper studies average consensus with random topologies (intermittent links) #R##N#\emph{and} noisy channels. Consensus with noise in the network links leads to the bias-variance dilemma--running consensus for long reduces the bias of the final average estimate but increases its variance. We present two different compromises to this tradeoff: the $\mathcal{A-ND}$ algorithm modifies conventional consensus by forcing the weights to satisfy a \emph{persistence} condition (slowly decaying to zero); and the $\mathcal{A-NC}$ algorithm where the weights are constant but consensus is run for a fixed number of iterations $\hat{\imath}$, then it is restarted and rerun for a total of $\hat{p}$ runs, and at the end averages the final states of the $\hat{p}$ runs (Monte Carlo averaging). We use controlled Markov processes and stochastic approximation arguments to prove almost sure convergence of $\mathcal{A-ND}$ to the desired average (asymptotic unbiasedness) and compute explicitly the m.s.e. (variance) of the consensus limit. We show that $\mathcal{A-ND}$ represents the best of both worlds--low bias and low variance--at the cost of a slow convergence rate; rescaling the weights...

Connected through hyperedge HE-1 (induced by source paper "multitask diffusion adaptation over networks with common latent representations"):
  with members:
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "distributed learning for stochastic generalized nash equilibrium problems"):
  with members:
    - "diffusion lms over multitask networks"
    - "multitask diffusion adaptation over asynchronous networks"
    - "multitask diffusion adaptation over networks"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability 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"

Connected through hyperedge HE-3 (induced by source paper "condense a reconfigurable knowledge acquisition architecture for future 5g iot"):
  with members:
    - "network coding as a service"
    - "photonic delay systems as machine learning implementations"
    - "software defined networking a comprehensive survey"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "distributed function computation over a rooted directed tree"
    - "linear coding schemes for the distributed computation of subspaces"
    - "internet of things iot a vision architectural elements and future directions"
    - "on network coding for sum networks"

Connected through hyperedge HE-4 (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"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "distributing the kalman filter for large scale systems"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-5 (induced by source paper "diffusion lms over multitask networks"):
  with members:
    - "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"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"

Connected through hyperedge HE-6 (induced by source paper "information sharing over adaptive networks with self interested agents"):
  with members:
    - "sharing in networks of strategic agents"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"
    - "performance limits for distributed estimation over lms adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"

Connected through hyperedge HE-7 (induced by source paper "information exchange and learning dynamics over weakly connected adaptive networks"):
  with members:
    - "distributed detection and estimation in wireless sensor networks"
    - "multitask diffusion adaptation over networks"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "distributed strongly convex optimization"
    - "higher dimensional consensus learning in large scale networks"

Connected through hyperedge HE-8 (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"

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)