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: "reasoning at the right time granularity"
  Abstract: Most real-world dynamic systems are composed of different components that often evolve at very different rates. In traditional temporal graphical models, such as dynamic Bayesian networks, time is modeled at a fixed granularity, generally selected based on the rate at which the fastest component evolves. Inference must then be performed at this fastest granularity, potentially at significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid time-slicing in the representation by modeling the system as evolving continuously over time. The expectation-propagation (EP) inference algorithm of Nodelman et al. (2005) can then vary the inference granularity over time, but the granularity is uniform across all parts of the system, and must be selected in advance. In this paper, we provide a new EP algorithm that utilizes a general cluster graph architecture where clusters contain distributions that can overlap in both space (set of variables) and time. This architecture allows different parts of the system to be modeled at very different time granularities, according to their current rate of evolution. We also provide an information-theoretic criterion for dynamically re-partitioning the clusters during inference to tune the level of approximation to the current rate of evolution. This avoids the need to hand-select the appropriate granularity, and allows the granularity to adapt as information is transmitted across the network. We present experiments demonstrating that this approach can result in significant computational savings.

Connected through hyperedge HE-1 (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-2 (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"
    - "expectation propagation for continuous time bayesian networks"
    - "dimension reduction in singularly perturbed continuous time bayesian networks"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "learning continuous time social network dynamics"):
  with members:
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "gibbs sampling in factorized continuous time markov processes"

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"
    - "ct nor representing and reasoning about events in continuous time"

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)