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: "asynchronous adaptation and learning over networks part ii performance analysis"
  Abstract: In Part I \cite{Zhao13TSPasync1}, we introduced a fairly general model for asynchronous events over adaptive networks including random topologies, random link failures, random data arrival times, and agents turning on and off randomly. We performed a stability analysis and established the notable fact that the network is still able to converge in the mean-square-error sense to the desired solution. Once stable behavior is guaranteed, it becomes important to evaluate how fast the iterates converge and how close they get to the optimal solution. This is a demanding task due to the various asynchronous events and due to the fact that agents influence each other. In this Part II, we carry out a detailed analysis of the mean-square-error performance of asynchronous strategies for solving distributed optimization and adaptation problems over networks. We derive analytical expressions for the mean-square convergence rate and the steady-state mean-square-deviation. The expressions reveal how the various parameters of the asynchronous behavior influence network performance. In the process, we establish the interesting conclusion that even under the influence of asynchronous events, all agents in the adaptive network can still reach an $O(\nu^{1 + \gamma_o'})$ near-agreement with some $\gamma_o' > 0$ while approaching the desired solution within $O(\nu)$ accuracy, where $\nu$ is proportional to the small step-size parameter for adaptation.

Connected through hyperedge HE-1 (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 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"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "distributing the kalman filter for large scale systems"

Connected through hyperedge HE-2 (induced by source paper "distributed gradient methods with variable number of working nodes"):
  with members:
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
    - "on the influence of informed agents on learning and adaptation over networks"
    - "asynchronous adaptation and learning over networks part iii comparison analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "hybrid deterministic stochastic methods for data fitting"
    - "a fast distributed proximal gradient method"
    - "fast distributed gradient methods"
    - "convergence rates of inexact proximal gradient methods for convex optimization"

Connected through hyperedge HE-3 (induced by source paper "diffusion lms for multitask problems with local linear equality constraints"):
  with members:
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "dictionary learning over distributed models"
    - "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"
    - "multitask diffusion adaptation over asynchronous networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "on the mean square performance of the constrained lms algorithm"

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 i modeling and stability 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"
    - "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"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "distributed policy evaluation under multiple behavior strategies"):
  with members:
    - "distributed pareto optimization via diffusion strategies"
    - "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"
    - "performance limits for distributed estimation over lms adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "multi timescale nexting in a reinforcement learning robot"
    - "predictive state temporal difference learning"

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 i modeling and stability analysis"
    - "diffusion adaptation over networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "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"

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.SY(Systems and Control)