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 "mean field variational approximation for continuous time bayesian networks"):
  Abstract: Continuous-time Bayesian networks is a natural structured representation language for multicomponent stochastic processes that evolve continuously over time. Despite the compact representation, inference in such models is intractable even in relatively simple structured networks. Here we introduce a mean field variational approximation in which we use a product of inhomogeneous Markov processes to approximate a distribution over trajectories. This variational approach leads to a globally consistent distribution, which can be efficiently queried. Additionally, it provides a lower bound on the probability of observations, thus making it attractive for learning tasks. We provide the theoretical foundations for the approximation, an efficient implementation that exploits the wide range of highly optimized ordinary differential equations (ODE) solvers, experimentally explore characterizations of processes for which this approximation is suitable, and show applications to a large-scale realworld inference problem.

Includes members:
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gru ode bayes continuous modeling of sporadically observed time series")
  Member "learning continuous time social network dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "listen to your face inferring facial action units from audio channel")
  Member "learning continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "listen to your face inferring facial action units from audio channel")
  Member "continuous time markov networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gibbs sampling in factorized continuous time markov processes")
  Member "residual belief propagation informed scheduling for asynchronous message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "expectation maximization and complex duration distributions for continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a hidden absorbing semi markov model for informatively censored temporal data learning and inference")
  Member "expectation propagation for continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ctbnctoolkit continuous time bayesian network classifier toolkit")

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.AI(Artificial Intelligence)