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 network structure from information cascades"):
  Abstract: Contagion processes are strongly linked to the network structures on which they propagate, and learning these structures is essential for understanding and intervention on complex network processes such as epidemics and (mis)information propagation. However, using contagion data to infer network structure is a challenging inverse problem. In particular, it is imperative to have appropriate measures of uncertainty in network structure estimates, however these are largely ignored in most machine-learning approaches. We present a probabilistic framework that uses samples from the distribution of networks that are compatible with the dynamics observed to produce network and uncertainty estimates. We demonstrate the method using the well known independent cascade model to sample from the distribution of networks P(G) conditioned on the observation of a set of infections C. We evaluate the accuracy of the method by using the marginal probabilities of each edge in the distribution, and show the bene ts of quantifying uncertainty to improve estimates and understanding, particularly with small amounts of data.

Includes members:
  Member "super blockers and the effect of network structure on information cascades":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating connected random graphs")
  Member "generating connected random graphs":
    also connected through other hyperedges:
      (none)
  Member "estimating network structure from unreliable measurements":
    also connected through other hyperedges:
      (none)
  Member "dynamic network model from partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential edge clustering in temporal multigraphs")
  Member "reconstructing networks with unknown and heterogeneous errors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "democratic summary of public opinions in free response surveys")
  Member "smerc social media event response clustering using textual and temporal information":
    also connected through other hyperedges:
      (none)
  Member "dani a fast diffusion aware network inference algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when less is more systematic analysis of cascade based community detection")
  Member "the flip markov chain for connected regular graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generating connected random graphs")

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)