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 i modeling and stability analysis"
  Abstract: In this work and the supporting Parts II [2] and III [3], we provide a rather detailed analysis of the stability and performance of asynchronous strategies for solving distributed optimization and adaptation problems over networks. We examine asynchronous networks that are subject to fairly general sources of uncertainties, such as changing topologies, random link failures, random data arrival times, and agents turning on and off randomly. Under this model, agents in the network may stop updating their solutions or may stop sending or receiving information in a random manner and without coordination with other agents. We establish in Part I conditions on the first and second-order moments of the relevant parameter distributions to ensure mean-square stable behavior. We derive in Part II expressions that reveal how the various parameters of the asynchronous behavior influence network performance. We compare in Part III the performance of asynchronous networks to the performance of both centralized solutions and synchronous networks. One notable conclusion is that the mean-square-error performance of asynchronous networks shows a degradation only of the order of $O(\nu)$, where $\nu$ is a small step-size parameter, while the convergence rate remains largely unaltered. The results provide a solid justification for the remarkable resilience of cooperative networks in the face of random failures at multiple levels: agents, links, data arrivals, and topology.

Connected through hyperedge HE-1 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis"):
  with members:
    - "adaptation and learning over networks under subspace constraints part ii performance analysis"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "network lasso clustering and optimization in large graphs"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "multitask diffusion adaptation over networks"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "adaptation and learning over networks under subspace constraints part ii performance analysis"):
  with members:
    - "adaptation and learning over networks under subspace constraints part i stability analysis"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "diffusion lms over multitask networks"
    - "distributed pareto optimization via diffusion strategies"

Connected through hyperedge HE-3 (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 ii performance 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-4 (induced by source paper "asyspa an exact asynchronous algorithm for convex optimization over digraphs"):
  with members:
    - "distributed discrete time optimization in multiagent networks using only sign of relative state"
    - "asynchronous decentralized optimization in directed networks"
    - "asynchronous subgradient push"
    - "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "asynchronous distributed optimization via randomized dual proximal gradient"
    - "average consensus on general strongly connected digraphs"

Connected through hyperedge HE-5 (induced by source paper "diff dac distributed actor critic for multitask deep reinforcement learning"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "distral robust multitask reinforcement learning"
    - "proximal policy optimization algorithms"
    - "connecting generative adversarial networks and actor critic methods"
    - "asynchronous methods for deep reinforcement learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "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"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "adaptive diffusion schemes for heterogeneous networks"):
  with members:
    - "diffusion lms over multitask networks"
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "on the influence of informed agents on learning and adaptation over networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-8 (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 ii performance 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"

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)