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 "factored filtering of continuous time systems"):
  Abstract: We consider filtering for a continuous-time, or asynchronous, stochastic system where the full distribution over states is too large to be stored or calculated. We assume that the rate matrix of the system can be compactly represented and that the belief distribution is to be approximated as a product of marginals. The essential computation is the matrix exponential. We look at two different methods for its computation: ODE integration and uniformization of the Taylor expansion. For both we consider approximations in which only a factored belief state is maintained. For factored uniformization we demonstrate that the KL-divergence of the filtering is bounded. Our experimental results confirm our factored uniformization performs better than previously suggested uniformization methods and the mean field algorithm.

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 "expectation maximization and complex duration distributions for continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a hidden absorbing semi markov model for informatively censored temporal data learning and inference")
  Member "mean field variational approximation for continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a variational perturbative approach to planning in graph based markov decision processes")
  Member "intrusion detection using continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "repad real time proactive anomaly detection for time series")

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.SY(Systems and Control)