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: "ct nor representing and reasoning about events in continuous time"
  Abstract: We present a generative model for representing and reasoning about the relationships among events in continuous time. We apply the model to the domain of networked and distributed computing environments where we fit the parameters of the model from timestamp observations, and then use hypothesis testing to discover dependencies between the events and changes in behavior for monitoring and diagnosis. After introducing the model, we present an EM algorithm for fitting the parameters and then present the hypothesis testing approach for both dependence discovery and change-point detection. We validate the approach for both tasks using real data from a trace of network events at Microsoft Research Cambridge. Finally, we formalize the relationship between the proposed model and the noisy-or gate for cases when time can be discretized.

Connected through hyperedge HE-1 (induced by source paper "a multi channel neural graphical event model with negative evidence"):
  with members:
    - "time dependent representation for neural event sequence prediction"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "modeling events with cascades of poisson processes"

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"
    - "expectation propagation for continuous time bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "modeling events with cascades of poisson processes"):
  with members:
    - "continuous time bayesian networks"
    - "expectation maximization and complex duration distributions for continuous time bayesian networks"

Connected through hyperedge HE-4 (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"
    - "expectation propagation for continuous time bayesian networks"
    - "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)