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 "random graph models for dynamic networks"):
  Abstract: Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.

Includes members:
  Member "stability of spreading processes over time varying large scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stability of sis spreading processes in networks with non markovian transmission and recovery")
  Member "clustering network layers with the strata multilayer stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk")
  Member "stochastic block transition models for dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a statistical model for dynamic networks with neural variational inference")
  Member "nonparametric multi group membership model for dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "query based interactive recommendation by meta path and adapted attention gru")
  Member "dynamic stochastic blockmodels statistical models for time evolving networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic network embedding via incremental skip gram with negative sampling")
  Member "subgraph frequencies mapping the empirical and extremal geography of large graph collections":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parallel algorithms for small subgraph counting")

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)