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: "continuous time bayesian networks"
  Abstract: In this paper we present a language for finite state continuous time Bayesian networks (CTBNs), which describe structured stochastic processes that evolve over continuous time. The state of the system is decomposed into a set of local variables whose values change over time. The dynamics of the system are described by specifying the behavior of each local variable as a function of its parents in a directed (possibly cyclic) graph. The model specifies, at any given point in time, the distribution over two aspects: when a local variable changes its value and the next value it takes. These distributions are determined by the variable s CURRENT value AND the CURRENT VALUES OF its parents IN the graph.More formally, each variable IS modelled AS a finite state continuous time Markov process whose transition intensities are functions OF its parents.We present a probabilistic semantics FOR the language IN terms OF the generative model a CTBN defines OVER sequences OF events.We list types OF queries one might ask OF a CTBN, discuss the conceptual AND computational difficulties associated WITH exact inference, AND provide an algorithm FOR approximate inference which takes advantage OF the structure within the process.

Connected through hyperedge HE-1 (induced by source paper "gru ode bayes continuous modeling of sporadically observed time series"):
  with members:
    - "early recognition of sepsis with gaussian process temporal convolutional networks and dynamic time warping"
    - "latent odes for irregularly sampled time series"
    - "deep ensemble tensor factorization for longitudinal patient trajectories classification"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "scalable and accurate deep learning for electronic health records"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"

Connected through hyperedge HE-2 (induced by source paper "optimizing city scale traffic flows through modeling isolated observations of vehicle movements"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "d4d senegal the second mobile phone data for development challenge"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"

Connected through hyperedge HE-3 (induced by source paper "reference model of multi entity bayesian networks for predictive situation awareness"):
  with members:
    - "context reasoning in underwater robots using mebn"
    - "hypothesis management in situation specific network construction"

Connected through hyperedge HE-4 (induced by source paper "listen to your face inferring facial action units from audio channel"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep learning using linear support vector machines"
    - "learning continuous time social network dynamics"
    - "learning continuous time bayesian networks"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-5 (induced by source paper "on the semantics and complexity of probabilistic logic programs"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "a new approach to updating beliefs"
    - "probabilistic reasoning with answer sets"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-6 (induced by source paper "formalizing neurath s ship approximate algorithms for online causal learning"):
  with members:
    - "a closed form expression for the sharma mittal entropy of exponential families"

Connected through hyperedge HE-7 (induced by source paper "doctor ai predicting clinical events via recurrent neural networks"):
  with members:
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "deep learning in neural networks"
    - "visualizing and understanding recurrent networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "how transferable are features in deep neural networks"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "lsda large scale detection through adaptation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "ctbnctoolkit continuous time bayesian network classifier toolkit"):
  with members:
    - "modeling events with cascades of poisson processes"
    - "learning continuous time bayesian networks"
    - "expectation propagation for continuous time bayesian networks"
    - "objective criteria for the evaluation of clustering methods"

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)