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 analysis for regular wireless consensus networks"
  Abstract: They proved that running the consensus for long time reduces the bias of the final average estimate but increases its variance. A closed form expression for the mean square error of the state and the optimum choice of parameters has been derived in [6] to guarantee fastest convergence. Consensus on small world and ramanujan networks has been studied in the [14], [15] and it has been proved that convergence rate is maximized in these topologies. Optimal topologies from the all possible topologies which increase the convergence rate has been studied in [18]. In our work, we study the convergence of the consensus algorithm for distance-regular networks with varying number of nearest neighbors. These networks represents the notion of geographical proximity in the wireless sensor networks. The main motivation for using the regular graph model is most of the practical networks are finite sized which cannot be studied by asymptotic results existing in the literature. Random geometric graphs which exactly depict the behavior of WSNs behave asymptotically as a regular graph [13], which are also convenient to analyze the wireless networks [11], [12], [17]. Our results are more precise and can be applied to most of the practical wireless networks, which are of finite sized, whereas asymptotic analyses are only applicable to very large sized networks. In r-nearest neighbor cycle and torus, an edge will be existed between every pair of neighbors that are r hops away. If a node’s transmission radius is increased, it will able to communicate with more number of nodes. Under this assumption, we have considered a variable r as the transmission radius. Node’s transmission radius or nearest neighbors will greatly effect the convergence rate of consensus algorithms. Effect of communication parameters on consensus algorithm’s convergence rate has been studied analytically in [10]. But this work does not provide the exact formulation of the convergence time of the consensus algorithms.

Connected through hyperedge HE-1 (induced by source paper "analysis of network robustness for finite sized wireless sensor networks"):
  with members:
    - "graph measures and network robustness"

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.DC(Distributed, Parallel, and Cluster Computing)