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 "a dynamic network model with persistent links and node specific latent variables with an application to the interbank market"):
  Abstract: We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links. Assuming a Markov dynamics for both mechanisms, we propose an Expectation-Maximization algorithm for model estimation and inference of the latent variables. The estimated parameters and fitnesses can be used to forecast the presence of a link in the future. We apply our methodology to the e-MID interbank network for which the two linkage mechanisms are associated with two different trading behaviors in the process of network formation, namely preferential trading and trading driven by node-specific characteristics. The empirical results allow to recognise preferential lending in the interbank market and indicate how a method that does not account for time-varying network topologies tends to overestimate preferential linkage.

Includes members:
  Member "link persistence and conditional distances in multiplex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active re identification attacks on periodically released dynamic social graphs")
  Member "random graph models for dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian graph convolutional neural networks using node copying")
  Member "disentangling group and link persistence in dynamic stochastic block models":
    also connected through other hyperedges:
      (none)
  Member "modelling sequences and temporal networks with dynamic community structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computational human dynamics")
  Member "dynamic stochastic blockmodels for time evolving social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational graph recurrent neural networks")
  Member "stochastic block transition models for dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a statistical model for dynamic networks with neural variational inference")

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)