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 propogation for the generative aspect model"
  Abstract: The generative aspect model is an extension of the multinomial model for text that allows word probabilities to vary stochastically across documents. Previous results with aspect models have been promising, but hindered by the computational difficulty of carrying out inference and learning. This paper demonstrates that the simple variational methods of Blei et al (2001) can lead to inaccurate inferences and biased learning for the generative aspect model. We develop an alternative approach that leads to higher accuracy at comparable cost. An extension of Expectation-Propagation is used for inference and then embedded in an EM algorithm for learning. Experimental results are presented for both synthetic and real data sets.

Connected through hyperedge HE-1 (induced by source paper "improving genomics based predictions for precision medicine through active elicitation of expert knowledge"):
  with members:
    - "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
    - "regression with n 1 by expert knowledge elicitation"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-2 (induced by source paper "what is wrong with topic modeling and how to fix it using search based software engineering"):
  with members:
    - "easy over hard a case study on deep learning"
    - "tuning for software analytics"
    - "mllib machine learning in apache spark"
    - "on smoothing and inference for topic models"

Connected through hyperedge HE-3 (induced by source paper "heron inference for bayesian graphical models"):
  with members:
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "same but different fast and high quality gibbs parameter estimation"
    - "a topic modeling toolbox using belief propagation"
    - "rethinking collapsed variational bayes inference for lda"

Connected through hyperedge HE-4 (induced by source paper "latent dirichlet allocation lda and topic modeling models applications a survey"):
  with members:
    - "improving topic models with latent feature word representations"
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "fast online em for big topic modeling"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "unveiling the political agenda of the european parliament plenary a topical analysis"
    - "et lda joint topic modeling for aligning events and their twitter feedback"
    - "the author topic model for authors and documents"
    - "modeling online reviews with multi grain topic models"

Connected through hyperedge HE-5 (induced by source paper "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"):
  with members:
    - "regression with n 1 by expert knowledge elicitation"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "preference elicitation for general random utility models"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-6 (induced by source paper "automatic text summarization approaches to speed up topic model learning process"):
  with members:
    - "probabilistic latent semantic analysis"
    - "probabilistic models for unified collaborative and content based recommendation in sparse data environments"
    - "artex is another text summarizer"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-7 (induced by source paper "learning and free energies for vector approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "the mutual information in random linear estimation"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "binary linear classification and feature selection via generalized approximate message passing"

Connected through hyperedge HE-8 (induced by source paper "feature extraction using latent dirichlet allocation and neural networks a case study on movie synopses"):
  with members:
    - "learning multilingual word representations using a bag of words autoencoder"
    - "distributed representations of sentences and documents"
    - "probabilistic latent semantic analysis"
    - "distributional semantics beyond words supervised learning of analogy and paraphrase"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "probabilistic models for unified collaborative and content based recommendation in sparse data environments"
    - "from frequency to meaning vector space models of semantics"

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)