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: "the bayesian structural em algorithm"
  Abstract: In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete data- that is, in the presence of missing values or hidden variables. In a recent paper, I introduced an algorithm called Structural EM that combines the standard Expectation Maximization (EM) algorithm, which optimizes parameters, with structure search for model selection. That algorithm learns networks based on penalized likelihood scores, which include the BIC/MDL score and various approximations to the Bayesian score. In this paper, I extend Structural EM to deal directly with Bayesian model selection. I prove the convergence of the resulting algorithm and show how to apply it for learning a large class of probabilistic models, including Bayesian networks and some variants thereof.

Connected through hyperedge HE-1 (induced by source paper "logical interpretations of autoencoders"):
  with members:
    - "learning logistic circuits"
    - "deepproblog neural probabilistic logic programming"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"
    - "emnist an extension of mnist to handwritten letters"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "programming with a differentiable forth interpreter"

Connected through hyperedge HE-2 (induced by source paper "learning factored markov decision processes with unawareness"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "sequential update of bayesian network structure"
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-3 (induced by source paper "reasoning about unforeseen possibilities during policy learning"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "grounding symbols in multi modal instructions"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "training an adaptive dialogue policy for interactive learning of visually grounded word meanings"
    - "sequential update of bayesian network structure"
    - "representation learning a review and new perspectives"
    - "reasoning about knowledge of unawareness revisited"
    - "learning to learn"

Connected through hyperedge HE-4 (induced by source paper "efficient learning of bounded treewidth bayesian networks from complete and incomplete data sets"):
  with members:
    - "learning bayesian networks with local structure"
    - "bayesian network learning with cutting planes"
    - "join graph propagation algorithms"

Connected through hyperedge HE-5 (induced by source paper "learning scripts as hidden markov models"):
  with members:
    - "probabilistic frame induction"

Connected through hyperedge HE-6 (induced by source paper "learning bayesian networks with incomplete data by augmentation"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "learning bayesian networks from incomplete databases"

Connected through hyperedge HE-7 (induced by source paper "parameter free probabilistic api mining across github"):
  with members:
    - "learning natural coding conventions"
    - "the long and the short of it summarising event sequences with serial episodes"

Connected through hyperedge HE-8 (induced by source paper "structure learning of probabilistic logic programs by searching the clause space"):
  with members:
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "unachievable region in precision recall space and its effect on empirical evaluation"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "well definedness and efficient inference for probabilistic logic programming under the distribution semantics"
    - "on the implementation of the probabilistic logic programming language problog"
    - "cp logic a language of causal probabilistic events and its relation to logic programming"
    - "parameter learning of logic programs for symbolic statistical modeling"

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)