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: "convergence time for unbiased quantized consensus over static and dynamic networks"
  Abstract: In this paper, the question of expected time to convergence is addressed for unbiased quantized consensus on undirected connected graphs, and some strong results are obtained. The paper first provides a tight expression for the expected convergence time of the unbiased quantized consensus over general but fixed networks. It is shown that the maximum expected convergence time lies within a constant factor of the maximum hitting time of an appropriate lazy random walk, using the theory of harmonic functions for reversible Markov chains. Following this, and using electric resistance analogy of the reversible Markov chains, the paper provides an upper bound of    $O(nmD\log n)$   for the expected convergence time to consensus, where    $n$  ,    $m$   and    $D$  , denote, respectively, the number of nodes, the number of edges, and the diameter of the network. Moreover, the paper shows that the above upper bound is tight up to a factor of    $\log n$   for some simple graphs such as line graph and cycle. Finally, the results are extended to bound the expected convergence time of the underlying dynamics in time-varying networks. Modeling such dynamics as the evolution of a time inhomogeneous Markov chain, the paper derives an upper bound for the expected convergence time of the dynamics using the spectral representation of the networks. This upper bound is significantly better than earlier ones given for different quantized consensus protocols over time-varying graphs.

Connected through hyperedge HE-1 (induced by source paper "robotic surveillance based on the meeting time of random walks"):
  with members:
    - "stochastic surveillance strategies for spatial quickest detection"
    - "coalescing random walks and voting on connected graphs"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-2 (induced by source paper "resilient randomized quantized consensus"):
  with members:
    - "robustness and algebraic connectivity of random interdependent networks"
    - "on the strengths of connectivity and robustness in general random intersection graphs"
    - "ergodic randomized algorithms and dynamics over networks"
    - "distributed randomized algorithms for the pagerank computation"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"
    - "convergence time analysis of quantized gossip consensus on digraphs"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-3 (induced by source paper "tight bounds for asymptotic and approximate consensus"):
  with members:
    - "approximate consensus in highly dynamic networks the role of averaging algorithms"

Connected through hyperedge HE-4 (induced by source paper "fast convergence of quantized consensus using metropolis chains over static and dynamic networks"):
  with members:
    - "consensus networks over finite fields"
    - "an upper bound on the convergence time for distributed binary consensus"
    - "convergence time analysis of quantized gossip consensus on digraphs"

Connected through hyperedge HE-5 (induced by source paper "convergence time of quantized metropolis consensus over time varying networks"):
  with members:
    - "an upper bound on the convergence time for distributed binary consensus"
    - "convergence time analysis of quantized gossip consensus on digraphs"

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)