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: "online belief propagation for topic modeling"
  Abstract: The batch latent Dirichlet allocation (LDA) algorithms play important roles in probabilistic topic modeling, but they are not suitable for processing big data streams due to high time and space compleixty. Online LDA algorithms can not only extract topics from big data streams with constant memory requirements, but also detect topic shifts as the data stream flows. In this paper, we present a novel and easy-to-implement online belief propagation (OBP) algorithm that infers the topic distribution from the previously unseen documents incrementally within the stochastic approximation framework. We discuss intrinsic relations between OBP and online expectation-maximization (OEM) algorithms, and show that OBP can converge to the local stationary point of the LDA's likelihood function. Extensive empirical studies confirm that OBP significantly reduces training time and memory usage while achieves a much lower predictive perplexity when compared with current state-of-the-art online LDA algorithms. Due to its ease of use, fast speed and low memory usage, OBP is a strong candidate for becoming the standard online LDA algorithm.

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"
    - "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-2 (induced by source paper "residual 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 topic modeling toolbox using 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.LG(Machine Learning)