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: "residual belief propagation for topic modeling"
  Abstract: Fast convergence speed is a desired property for training latent Dirichlet allocation (LDA), especially in online and parallel topic modeling for massive data sets. This paper presents a novel residual belief propagation (RBP) algorithm to accelerate the convergence speed for training LDA. The proposed RBP uses an informed scheduling scheme for asynchronous message passing, which passes fast-convergent messages with a higher priority to influence those slow-convergent messages at each learning iteration. Extensive empirical studies confirm that RBP significantly reduces the training time until convergence while achieves a much lower predictive perplexity than other state-of-the-art training algorithms for LDA, including variational Bayes (VB), collapsed Gibbs sampling (GS), loopy belief propagation (BP), and residual VB (RVB).

Connected through hyperedge HE-1 (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-2 (induced by source paper "peacock learning long tail topic features for industrial applications"):
  with members:
    - "fast online em for big topic modeling"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards big topic modeling"):
  with members:
    - "fast online em for big topic modeling"
    - "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"

Connected through hyperedge HE-4 (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"
    - "using variational inference and mapreduce to scale topic modeling"

Connected through hyperedge HE-5 (induced by source paper "online belief propagation for topic modeling"):
  with members:
    - "learning topic models by belief propagation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a topic modeling toolbox using belief propagation"):
  with members:
    - "learning topic models by belief propagation"
    - "memory efficient topic modeling"
    - "on smoothing and inference for topic models"
    - "a new approach to speeding up topic modeling"
    - "the author topic model for authors and documents"

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)