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: "learning 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 parameters and structure of a CTBN from fully observed data. We define a conjugate prior for CTBNs, and show how it can be used both for Bayesian parameter estimation and as the basis of a Bayesian score for structure learning. Because acyclicity is not a constraint in CTBNs, we can show that the structure learning problem is significantly easier, both in theory and in practice, than structure learning for dynamic Bayesian networks (DBNs). Furthermore, as CTBNs can tailor the parameters and dependency structure to the different time granularities of the evolution of different variables, they can provide a better fit to continuous-time processes than DBNs with a fixed time granularity.

Connected through hyperedge HE-1 (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"
    - "continuous time bayesian networks"
    - "learning continuous time social network dynamics"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-2 (induced by source paper "ctbnctoolkit continuous time bayesian network classifier toolkit"):
  with members:
    - "modeling events with cascades of poisson processes"
    - "continuous time bayesian networks"
    - "expectation propagation for continuous time bayesian networks"
    - "objective criteria for the evaluation of clustering methods"

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

Connected through hyperedge HE-4 (induced by source paper "gibbs sampling in factorized continuous time markov processes"):
  with members:
    - "expectation propagation for approximate bayesian inference"
    - "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"
    - "reasoning at the right time granularity"

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"
    - "expectation maximization and complex duration distributions for continuous time bayesian networks"
    - "expectation propagation for continuous time bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "asymmetric separation for local independence graphs"):
  with members:
    - "identifying independencies in causal graphs with feedback"
    - "causal networks semantics and expressiveness"
    - "directed cyclic graphical representations of feedback models"
    - "continuous time bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"):
  with members:
    - "display of information for time critical decision making"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "learning bayesian networks a unification for discrete and gaussian domains"
    - "staged mixture modelling and boosting"

Connected through hyperedge HE-8 (induced by source paper "learning continuous time social network dynamics"):
  with members:
    - "continuous time bayesian networks"
    - "gibbs sampling in factorized continuous time markov processes"
    - "reasoning at the right time granularity"

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.LG(Machine Learning)