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: "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
  Abstract: Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes interact with each other on a local level and diffuse information across the network to solve estimation and inference tasks in a distributed manner. In this work, we compare the mean-square performance of two main strategies for distributed estimation over networks: consensus strategies and diffusion strategies. The analysis in the paper confirms that under constant step-sizes, diffusion strategies allow information to diffuse more thoroughly through the network and this property has a favorable effect on the evolution of the network: diffusion networks are shown to converge faster and reach lower mean-square deviation than consensus networks, and their mean-square stability is insensitive to the choice of the combination weights. In contrast, and surprisingly, it is shown that consensus networks can become unstable even if all the individual nodes are stable and able to solve the estimation task on their own. When this occurs, cooperation over the network leads to a catastrophic failure of the estimation task. This phenomenon does not occur for diffusion networks: we show that stability of the individual nodes always ensures stability of the diffusion network irrespective of the combination topology. Simulation results support the theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "a communication efficient multi agent actor critic algorithm for distributed reinforcement learning"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "on convergence of emphatic temporal difference learning"

Connected through hyperedge HE-2 (induced by source paper "distributed learning in non convex environments part ii polynomial escape from saddle points"):
  with members:
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "escaping saddles with stochastic gradients"
    - "how to escape saddle points efficiently"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "next in network nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the learning behavior of adaptive networks part ii performance analysis"

Connected through hyperedge HE-3 (induced by source paper "study of robust distributed diffusion rls algorithms with side information for adaptive networks"):
  with members:
    - "maximum total correntropy diffusion adaptation over networks with noisy links"
    - "decentralized rls with data adaptive censoring for regressions over large scale networks"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"
    - "robust adaptive beamforming using a low complexity shrinkage based mismatch estimation algorithm"
    - "direction finding algorithms based on joint iterative subspace optimization"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-4 (induced by source paper "distributed interference cancellation in multi agent scenarios"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "a distributional perspective on reinforcement learning"
    - "stationary signal processing on graphs"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "discrete signal processing on graphs sampling theory"
    - "diffusion lms over multitask networks"

Connected through hyperedge HE-5 (induced by source paper "robust distributed diffusion recursive least squares algorithms with side information for adaptive networks"):
  with members:
    - "maximum total correntropy diffusion adaptation over networks with noisy links"
    - "decentralized rls with data adaptive censoring for regressions over large scale networks"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-6 (induced by source paper "on the influence of bias correction on distributed stochastic optimization"):
  with members:
    - "next in network nonconvex optimization"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-7 (induced by source paper "detection under one bit messaging over adaptive networks"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed parameter estimation with quantized communication via running average"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "active sequential hypothesis testing"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"

Connected through hyperedge HE-8 (induced by source paper "dynamic average diffusion with randomized coordinate updates"):
  with members:
    - "supervised learning under distributed features"
    - "stochastic gradient push for distributed deep learning"
    - "coordinate descent diffusion learning by networked agents"
    - "distributed big data optimization via block iterative convexification and averaging"
    - "next in network nonconvex optimization"
    - "dictionary learning over distributed models"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "distributed pareto optimization via diffusion strategies"

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)