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: "learning continuous time social network dynamics"
  Abstract: We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of the model than the standard method of moments algorithmfromthe sociology literature. We extend the existing social network models to allow for indirect and asynchronous observations of the links. A Markov chain Monte Carlo sampling algorithm for this new model permits estimation and inference. We provide results on both a synthetic network (for verification) and real social network data.

Connected through hyperedge HE-1 (induced by source paper "listen to your face inferring facial action units from audio channel"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep learning using linear support vector machines"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-2 (induced by source paper "latent self exciting point process model for spatial temporal networks"):
  with members:
    - "modeling information propagation with survival theory"
    - "modeling events with cascades of poisson processes"
    - "structure and dynamics of information pathways in online media"
    - "information theoretic measures of influence based on content dynamics"
    - "information transfer in social media"

Connected through hyperedge HE-3 (induced by source paper "mean field variational approximation for continuous time bayesian networks"):
  with members:
    - "tractable inference for complex stochastic processes"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "continuous time markov networks"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "expectation maximization and complex duration distributions for continuous time bayesian networks"
    - "expectation propagation for continuous time bayesian networks"
    - "ct nor representing and reasoning about events in continuous time"

Connected through hyperedge HE-4 (induced by source paper "factored filtering of continuous time systems"):
  with members:
    - "tractable inference for complex stochastic processes"
    - "continuous time bayesian networks"
    - "expectation maximization and complex duration distributions for continuous time bayesian networks"
    - "mean field variational approximation for continuous time bayesian networks"
    - "intrusion detection using continuous time bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "co evolution of selection and influence in social networks"):
  with members:
    - "a generalized mean field algorithm for variational inference in exponential families"

Connected through hyperedge HE-6 (induced by source paper "intrusion detection using continuous time bayesian networks"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "expectation maximization and complex duration distributions for continuous time bayesian networks"
    - "mean field variational approximation for continuous time bayesian networks"
    - "expectation propagation for continuous time bayesian networks"
    - "ct nor representing and reasoning about events in continuous time"
    - "gibbs sampling in factorized continuous time markov processes"

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)