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: "tunable eigenvector based centralities for multiplex and temporal networks"
  Abstract: Characterizing the importances of nodes in social, biological, information and technological networks is a core topic for the network-science and data-science communities. We present a linear-algebraic framework that generalizes eigenvector-based centralities---including PageRank---to provide a common framework for two popular classes of multilayer networks: multiplex networks with layers encoding different types of relationships and temporal networks in which the relationships change in time. Our approach involves the study of joint, marginal, and conditional supracentralities that can be obtained from the dominant eigenvector of a supracentrality matrix [Taylor et al., 2017], which couples centrality matrices that are associated with the individual network layers. We extend this prior work (which was restricted to temporal networks with layers that are coupled by undirected, adjacent-in-time coupling) by allowing the layers to be coupled through a (possibly asymmetric) interlayer adjacency matrix $\tilde{\bf A}$ in which each entry gives the coupling between layers $t$ and $t'$. Our framework provides a unifying foundation for centrality analysis of multiplex and/or temporal networks and reveals a complicated dependency of the supracentralities on the topology of interlayer coupling. We scale $\tilde{\bf A}$ by a coupling strength $\omega\ge0$ and develop singular perturbation theory for the limits of weak ($\omega\to0^+$) and strong coupling ($\omega\to\infty$), revealing an interesting dependence on the dominant eigenvectors of $\tilde{\bf A}$. We apply the framework to two empirical data sets: a multiplex network representation of airline transportation in Europe, and a temporal network encoding the graduation and hiring of mathematicians at U.S. colleges and universities.

Connected through hyperedge HE-1 (induced by source paper "nonlinearity networks a 2020 vision"):
  with members:
    - "models of continuous time networks with tie decay diffusion and convection"
    - "architecture and evolution of semantic networks in mathematics texts"
    - "supracentrality analysis of temporal networks with directed interlayer coupling"
    - "using persistent homology to quantify a diurnal cycle in hurricane felix"
    - "persistent homology of geospatial data a case study with voting"
    - "network structure from rich but noisy data"
    - "random walks on simplicial complexes and the normalized hodge 1 laplacian"
    - "the role of network analysis in industrial and applied mathematics"

Connected through hyperedge HE-2 (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling"):
  with members:
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "a new framework for centrality measures in multiplex networks"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "node and layer eigenvector centralities for multiplex networks"
    - "post processing partitions to identify domains of modularity optimization"
    - "a new framework for dynamical models on multiplex networks"
    - "block matrix formulations for evolving networks"
    - "pagerank beyond the web"

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.SI(Social and Information Networks)