human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling"):
  Abstract: We describe centralities in temporal networks using a supracentrality framework to study centrality trajectories, which characterize how the importances of nodes change in time. We study supracentrality generalizations of eigenvector-based centralities, a family of centrality measures for time-independent networks that includes PageRank, hub and authority scores, and eigenvector centrality. We start with a sequence of adjacency matrices, each of which represents a time layer of a network at a different point or interval of time. Coupling centrality matrices across time layers with weighted interlayer edges yields a supracentrality matrix \(\mathbb {C}(\omega )\), where ω controls the extent to which centrality trajectories change over time. We can flexibly tune the weight and topology of the interlayer coupling to cater to different scientific applications. The entries of the dominant eigenvector of \(\mathbb {C}(\omega )\) represent joint centralities, which simultaneously quantify the importance of every node in every time layer. Inspired by probability theory, we also compute marginal and conditional centralities. We illustrate how to adjust the coupling between time layers to tune the extent to which nodes’ centrality trajectories are influenced by the oldest and newest time layers. We support our findings by analysis in the limits of small and large ω.

Includes members:
  Member "tunable eigenvector based centralities for multiplex and temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "relating modularity maximization and stochastic block models in multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models")
  Member "a new framework for centrality measures in multiplex networks":
    also connected through other hyperedges:
      (none)
  Member "distributed algorithms for computation of centrality measures in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "node and layer eigenvector centralities for multiplex networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a framework for second order eigenvector centralities and clustering coefficients")
  Member "post processing partitions to identify domains of modularity optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modularity belief propagation on multilayer networks to detect significant community structure")
  Member "a new framework for dynamical models on multiplex networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiplex communities and the emergence of international conflict")
  Member "block matrix formulations for evolving networks":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)