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: "a computational scheme for reasoning in dynamic probabilistic networks"
  Abstract: A computational scheme for reasoning about dynamic systems using (causal) probabilistic networks is presented. The scheme is based on the framework of Lauritzen and Spiegelhalter (1988), and may be viewed as a generalization of the inference methods of classical time-series analysis in the sense that it allows description of non-linear, multivariate dynamic systems with complex conditional independence structures. Further, the scheme provides a method for efficient backward smoothing and possibilities for efficient, approximate forecasting methods. The scheme has been implemented on top of the HUGIN shell.

Connected through hyperedge HE-1 (induced by source paper "learning the structure of dynamic probabilistic networks"):
  with members:
    - "structure and parameter learning for causal independence and causal interaction models"
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian networks with local structure"
    - "tractable inference for complex stochastic processes"
    - "the bayesian structural em algorithm"
    - "learning gaussian networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "structured arc reversal and simulation of dynamic probabilistic networks"):
  with members:
    - "sensor validation using dynamic belief networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "a graph theoretic analysis of information value"
    - "context specific independence in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "value directed belief state approximation for pomdps"):
  with members:
    - "approximate planning for factored pomdps using belief state simplification"
    - "tractable inference for complex stochastic processes"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "spudd stochastic planning using decision diagrams"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-4 (induced by source paper "tractable inference for complex stochastic processes"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "a scheme for approximating probabilistic inference"
    - "dynamic network models for forecasting"
    - "tradeoffs in constructing and evaluating temporal influence diagrams"

Connected through hyperedge HE-5 (induced by source paper "forecasting sleep apnea with dynamic network models"):
  with members:
    - "temporal reasoning with probabilities"
    - "additive belief network models"
    - "dynamic network models for forecasting"
    - "probabilistic causal reasoning"

Connected through hyperedge HE-6 (induced by source paper "probabilistic state dependent grammars for plan recognition"):
  with members:
    - "accounting for context in plan recognition with application to traffic monitoring"
    - "tractable inference for complex stochastic processes"
    - "a new model of plan recognition"

Connected through hyperedge HE-7 (induced by source paper "the factored frontier algorithm for approximate inference in dbns"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "tractable inference for complex stochastic processes"

Connected through hyperedge HE-8 (induced by source paper "stochastic simulation algorithms for dynamic probabilistic networks"):
  with members:
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"

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)