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: "using variational inference and mapreduce to scale topic modeling"
  Abstract: Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring document collections. Because of the increasing prevalence of large datasets, there is a need to improve the scalability of inference of LDA. In this paper, we propose a technique called MapReduce LDA (Mr. LDA) to accommodate very large corpus collections in the MapReduce framework. In contrast to other techniques to scale inference for LDA, which use Gibbs sampling, we use variational inference. Our solution efficiently distributes computation and is relatively simple to implement. More importantly, this variational implementation, unlike highly tuned and specialized implementations, is easily extensible. We demonstrate two extensions of the model possible with this scalable framework: informed priors to guide topic discovery and modeling topics from a multilingual corpus.

Connected through hyperedge HE-1 (induced by source paper "semi automatic terminology ontology learning based on topic modeling"):
  with members:
    - "an ontology based adaptive personalized e learning system assisted by software agents on cloud storage"
    - "a hybrid approach using ontology similarity and fuzzy logic for semantic question answering"
    - "topic words analysis based on lda model"
    - "algorithms of the lda model report"
    - "tensor decompositions for learning latent variable models"
    - "computing a nonnegative matrix factorization provably"

Connected through hyperedge HE-2 (induced by source paper "fast online em for big topic modeling"):
  with members:
    - "peacock learning long tail topic features for industrial applications"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "learning topic models by belief propagation"
    - "online belief propagation for topic modeling"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "communication efficient parallel belief propagation for latent dirichlet allocation"
    - "on smoothing and inference for topic models"
    - "a new approach to speeding up topic modeling"

Connected through hyperedge HE-3 (induced by source paper "topic similarity networks visual analytics for large document sets"):
  with members:
    - "kert automatic extraction and ranking of topical keyphrases from content representative document titles"
    - "exploratory analysis of highly heterogeneous document collections"
    - "learning algorithms for keyphrase extraction"

Connected through hyperedge HE-4 (induced by source paper "learning topic models by belief propagation"):
  with members:
    - "on smoothing and inference for topic models"
    - "the author topic model for authors and documents"
    - "a topic modeling toolbox using belief propagation"
    - "higher order markov tag topic models for tagged documents and images"

Connected through hyperedge HE-5 (induced by source paper "a new approach to speeding up topic modeling"):
  with members:
    - "learning topic models by belief propagation"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "memory efficient topic modeling"
    - "communication efficient parallel belief propagation for latent dirichlet allocation"
    - "on smoothing and inference for topic models"
    - "a topic modeling toolbox using belief propagation"
    - "sparse stochastic inference for latent dirichlet allocation"
    - "residual belief propagation for topic modeling"

Connected through hyperedge HE-6 (induced by source paper "communication efficient parallel belief propagation for latent dirichlet allocation"):
  with members:
    - "learning topic models by belief propagation"

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)