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: "fast online em for big topic modeling"
  Abstract: The expectation-maximization (EM) algorithm can compute the maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the mixture models or latent variable models such as latent Dirichlet allocation (LDA),which has been one of the most popular probabilistic topic modeling methods in the past decade. However, batch EM has high time and space complexities to learn big LDA models from big data streams. In this paper, we present a fast online EM (FOEM) algorithm that infers the topic distribution from the previously unseen documents incrementally with constant memory requirements. Within the stochastic approximation framework, we show that FOEM can converge to the local stationary point of the LDA’s likelihood function. By dynamic scheduling for the fast speed and parameter streaming for the low memory usage, FOEM is more efficient for some lifelong topic modeling tasks than the state-of-the-art online LDA algorithms to handle both big data and big models (aka, big topic modeling) on just a PC.

Connected through hyperedge HE-1 (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"
    - "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"
    - "expectation propogation for the generative aspect model"
    - "the author topic model for authors and documents"
    - "modeling online reviews with multi grain topic models"

Connected through hyperedge HE-2 (induced by source paper "peacock learning long tail topic features for industrial applications"):
  with members:
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "towards big topic modeling"
    - "learning topic models by belief propagation"
    - "expectation propogation for the generative aspect model"
    - "communication efficient parallel belief propagation for latent dirichlet allocation"
    - "on smoothing and inference for topic models"
    - "a new approach to speeding up topic modeling"
    - "a topic modeling toolbox using belief propagation"

Connected through hyperedge HE-3 (induced by source paper "towards big topic modeling"):
  with members:
    - "learning topic models by belief propagation"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "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"
    - "a topic modeling toolbox using belief propagation"
    - "sparse stochastic inference for latent dirichlet allocation"

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)