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: "on the learning behavior of adaptive networks part ii performance analysis"
  Abstract: Part I of this work examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results also identified three distinct stages in the learning behavior of multi-agent networks related to transient phases I and II and the steady-state phase. This Part II examines the steady-state phase of distributed learning by networked agents. Apart from characterizing the performance of the individual agents, it is shown that the network induces a useful equalization effect across all agents. In this way, the performance of noisier agents is enhanced to the same level as the performance of agents with less noisy data. It is further shown that in the small step-size regime, each agent in the network is able to achieve the same performance level as that of a centralized strategy corresponding to a fully connected network. The results in this part reveal explicitly which aspects of the network topology and operation influence performance and provide important insights into the design of effective mechanisms for the processing and diffusion of information over networks.

Connected through hyperedge HE-1 (induced by source paper "inverse graph learning over optimization networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "interplay between topology and social learning over weak graphs"
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"

Connected through hyperedge HE-2 (induced by source paper "linear speedup in saddle point escape for decentralized non convex optimization"):
  with members:
    - "distributed learning in non convex environments part ii polynomial escape from saddle points"
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "escaping saddles with stochastic gradients"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "next in network nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "communication efficient distributed dual coordinate ascent"

Connected through hyperedge HE-3 (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 i transient analysis"

Connected through hyperedge HE-4 (induced by source paper "non bayesian social learning with uncertain models"):
  with members:
    - "social learning over weakly connected graphs"
    - "defending non bayesian learning against adversarial attacks"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-5 (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"
    - "active sequential hypothesis testing"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-6 (induced by source paper "learning over multitask graphs part i stability analysis"):
  with members:
    - "learning over multitask graphs part ii performance analysis"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "signals on graphs uncertainty principle and sampling"
    - "network lasso clustering and optimization in large graphs"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-7 (induced by source paper "learning over multitask graphs part ii performance analysis"):
  with members:
    - "learning over multitask graphs part i stability analysis"
    - "diffusion lms for multitask problems with local linear equality constraints"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "diffusion lms over multitask networks"
    - "network lasso clustering and optimization in large graphs"
    - "learning laplacian matrix in smooth graph signal representations"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-8 (induced by source paper "local tomography of large networks under the low observability regime"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "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"
    - "on the learning behavior of adaptive networks part i transient analysis"

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.MA(Multiagent Systems)