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: "distributed estimation and control of node centrality in undirected asymmetric networks"
  Abstract: Measures of node centrality that describe the importance of a node within a network are crucial for understanding the behavior of social networks and graphs. In this paper, we address the problems of distributed estimation and control of node centrality in undirected graphs with asymmetric weight values. In particular, we focus our attention on $\alpha$-centrality, which can be seen as a generalization of eigenvector centrality, particularly suitable for graphs with asymmetric interactions. In this setting, our contribution is twofold: first we derive a distributed protocol where agents can locally compute their $\alpha$-centrality index by means of local interactions; then, we focus our attention on the problem of controlling the weight matrix of the graph in such a way that the network reaches a desired $\alpha$-centrality vector. The interest of our solution lies in obtaining the control objective with minimum changes in the original influence matrix. Moreover, with our algorithm every agent is able to reach the desired value locally and in finite-time. The two algorithms are then applied to two problems of interest in real life. The estimation method is used together with a consensus algorithm to achieve a consensus value weighted by the influence of each node in the network. The control algorithm is exploited to protect the most valuable nodes in a network against a targeted attack, by making every node in the network equally important in terms of {\alpha}-centrality. Simulations results are provided to corroborate the theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "simple and fast distributed computation of betweenness centrality"):
  with members:
    - "distributed algorithms for directed betweenness centrality and all pairs shortest paths"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "fully dynamic approximation of betweenness centrality"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"

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)