human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "peacock learning long tail topic features for industrial applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient correlated topic modeling with topic embedding")
  Member "stochastic collapsed variational bayesian inference for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topic discovery in massive text corpora based on min hashing")
  Member "learning topic models by belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "peacock learning long tail topic features for industrial applications")
  Member "online belief propagation for topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "residual belief propagation for topic modeling")
  Member "residual belief propagation informed scheduling for asynchronous message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "communication efficient parallel belief propagation for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "peacock learning long tail topic features for industrial applications")
  Member "on smoothing and inference for topic models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "topic grouper an agglomerative clustering approach to topic modeling")
  Member "a new approach to speeding up topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "peacock learning long tail topic features for industrial applications")

Question: Please tell me which class the source paper 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)