human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "online bayesian inference of diffusion networks"
  Abstract: Understanding the process by which a contagion disseminates throughout a network is of great importance in many real world applications. The required sophistication of the inference approach depends on the type of information we want to extract as well as the number of observations that are available to us. We analyze scenarios in which not only the underlying network structure (parental relationships and link strengths) needs to be detected, but also the infection times must be estimated. We assume that our only observation of the diffusion process is a set of time series, one for each node of the network, which exhibit changepoints when an infection occurs. After formulating a model to describe the contagion, and selecting appropriate prior distributions, we seek to find the set of model parameters that best explains our observations. Modeling the problem in a Bayesian framework, we exploit Monte Carlo Markov Chain, #R##N#Sequential Monte Carlo, and time series analysis techniques to develop batch and online inference algorithms. We evaluate the performance of our proposed algorithms via numerical simulations of synthetic network contagions and analysis of real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "bayesian inference of network structure from information cascades"):
  with members:
    - "super blockers and the effect of network structure on information cascades"
    - "generating connected random graphs"
    - "estimating network structure from unreliable measurements"
    - "dynamic network model from partial observations"
    - "reconstructing networks with unknown and heterogeneous errors"
    - "smerc social media event response clustering using textual and temporal information"
    - "dani a fast diffusion aware network inference algorithm"
    - "the flip markov chain for connected regular graphs"

Question: Please tell me which class the center node 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)