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 "bayesian inference of diffusion networks with unknown infection times"):
  Abstract: The analysis of diffusion processes in real-world propagation scenarios often involves estimating variables that are not directly observed. These hidden variables include parental relationships, the strengths of connections between nodes, and the moments of time that infection occurs. In this paper, we propose a framework in which all three sets of parameters are assumed to be hidden and we develop a Bayesian approach to infer them. After justifying the model assumptions, we evaluate the performance efficiency of our proposed approach through numerical simulations on synthetic datasets and real-world diffusion processes.

Includes members:
  Member "back to the past source identification in diffusion networks from partially observed cascades":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cascade size distributions and why they matter")
  Member "coevolve a joint point process model for information diffusion and network co evolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks")
  Member "structure and dynamics of information pathways in online media":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "uncovering the temporal dynamics of diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "inferring networks of diffusion and influence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep collaborative embedding for information cascade prediction")

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)