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: "expectation maximization and complex duration distributions for continuous time bayesian networks"
  Abstract: Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. We address the problem of learning the parameters and structure of a CTBN from partially observed data. We show how to apply expectation maximization (EM) and structural expectation maximization (SEM) to CTBNs. The availability of the EM algorithm allows us to extend the representation of CTBNs to allow a much richer class of transition durations distributions, known as phase distributions. This class is a highly expressive semi-parametric representation, which can approximate any duration distribution arbitrarily closely. This extension to the CTBN framework addresses one of the main limitations of both CTBNs and DBNs - the restriction to exponentially / geometrically distributed duration. We present experimental results on a real data set of people's life spans, showing that our algorithm learns reasonable models - structure and parameters - from partially observed data, and, with the use of phase distributions, achieves better performance than DBNs.

Connected through hyperedge HE-1 (induced by source paper "a hidden absorbing semi markov model for informatively censored temporal data learning and inference"):
  with members:
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "personalized risk scoring for critical care prognosis using mixtures of gaussian processes"
    - "input output non linear dynamical systems applied to physiological condition monitoring"
    - "balancing suspense and surprise timely decision making with endogenous information acquisition"
    - "recurrent neural networks for multivariate time series with missing values"

Connected through hyperedge HE-2 (induced by source paper "rankmap a platform aware framework for distributed learning from dense datasets"):
  with members:
    - "self expressive decompositions for matrix approximation and clustering"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "greedy feature selection for subspace clustering"
    - "sparse subspace clustering algorithm theory and applications"
    - "model based compressive sensing"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-3 (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 propagation for continuous time bayesian networks"
    - "ct nor representing and reasoning about events in continuous time"

Connected through hyperedge HE-4 (induced by source paper "factored filtering of continuous time systems"):
  with members:
    - "tractable inference for complex stochastic processes"
    - "continuous time bayesian networks"
    - "learning continuous time social network dynamics"
    - "mean field variational approximation for continuous time bayesian networks"
    - "intrusion detection using continuous time bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "continuous time markov networks"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"
    - "expectation propagation for continuous time bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "expectation propagation for continuous time bayesian networks"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "continuous time bayesian networks"
    - "learning continuous time bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "modeling events with cascades of poisson processes"):
  with members:
    - "continuous time bayesian networks"
    - "ct nor representing and reasoning about events in continuous time"

Connected through hyperedge HE-8 (induced by source paper "compositional stochastic modeling and probabilistic programming"):
  with members:
    - "continuous time bayesian networks"
    - "dolfin automated finite element computing"

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)