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: "expectation propagation for continuous time bayesian networks"
  Abstract: Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. As shown previously, exact inference in CTBNs is intractable. We address the problem of approximate inference, allowing for general queries conditioned on evidence over continuous time intervals and at discrete time points. We show how CTBNs can be parameterized within the exponential family, and use that insight to develop a message passing scheme in cluster graphs and allows us to apply expectation propagation to CTBNs. The clusters in our cluster graph do not contain distributions over the cluster variables at individual time points, but distributions over trajectories of the variables throughout a duration. Thus, unlike discrete time temporal models such as dynamic Bayesian networks, we can adapt the time granularity at which we reason for different variables and in different conditions.

Connected through hyperedge HE-1 (induced by source paper "ctbnctoolkit continuous time bayesian network classifier toolkit"):
  with members:
    - "modeling events with cascades of poisson processes"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-2 (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 social network dynamics"
    - "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"
    - "ct nor representing and reasoning about events in continuous time"

Connected through hyperedge HE-3 (induced by source paper "gibbs sampling in factorized continuous time markov processes"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "continuous time markov networks"
    - "dimension reduction in singularly perturbed continuous time bayesian networks"
    - "expectation propogation for approximate inference in dynamic bayesian networks"
    - "reasoning at the right time granularity"

Connected through hyperedge HE-4 (induced by source paper "continuous time markov networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "expectation maximization and complex duration distributions for continuous time bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "expectation maximization and complex duration distributions for continuous time bayesian networks"):
  with members:
    - "the bayesian structural em algorithm"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "reasoning at the right time granularity"):
  with members:
    - "nested junction trees"
    - "expectation propagation for approximate bayesian inference"
    - "continuous time bayesian networks"

Connected through hyperedge HE-7 (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 social network dynamics"
    - "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"
    - "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.AI(Artificial Intelligence)