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 "structured networks and coarse grained descriptions a dynamical perspective"):
  Abstract: This chapter discusses the interplay between structure and dynamics in complex networks. Given a particular network with an endowed dynamics, our goal is to find partitions aligned with the dynamical process acting on top of the network. We thus aim to gain a reduced description of the system that takes into account both its structure and dynamics. In the first part, we introduce the general mathematical setup for the types of dynamics we consider throughout the chapter. We provide two guiding examples, namely consensus dynamics and diffusion processes (random walks), motivating their connection to social network analysis, and provide a brief discussion on the general dynamical framework and its possible extensions. In the second part, we focus on the influence of graph structure on the dynamics taking place on the network, focusing on three concepts that allow us to gain insight into this notion. First, we describe how time scale separation can appear in the dynamics on a network as a consequence of graph structure. Second, we discuss how the presence of particular symmetries in the network give rise to invariant dynamical subspaces that can be precisely described by graph partitions. Third, we show how this dynamical viewpoint can be extended to study dynamics on networks with signed edges, which allow us to discuss connections to concepts in social network analysis, such as structural balance. In the third part, we discuss how to use dynamical processes unfolding on the network to detect meaningful network substructures. We then show how such dynamical measures can be related to seemingly different algorithm for community detection and coarse-graining proposed in the literature. We conclude with a brief summary and highlight interesting open future directions.

Includes members:
  Member "multiscale dynamical embeddings of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised classification on graphs using explicit diffusion dynamics")
  Member "a tutorial on modeling and analysis of dynamic social networks part i":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ideotrace a framework for ideology tracing with a case study on the 2016 u s presidential election")
  Member "the many facets of community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "embedding based silhouette community detection")
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "opinion dynamics in social networks with hostile camps consensus vs polarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "percolation threshold for competitive influence in random networks")
  Member "seismic a self exciting point process model for predicting tweet popularity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction")
  Member "spectra of random graphs with community structure and arbitrary degrees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "community detectability and structural balance dynamics in signed networks")
  Member "spectral redemption in clustering sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)