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: "communication efficient parallel belief propagation for latent dirichlet allocation"
  Abstract: This paper presents a novel communication-efficient parallel belief propagation (CE-PBP) algorithm for training latent Dirichlet allocation (LDA). Based on the synchronous belief propagation (BP) algorithm, we first develop a parallel belief propagation (PBP) algorithm on the parallel architecture. Because the extensive communication delay often causes a low efficiency of parallel topic modeling, we further use Zipf's law to reduce the total communication cost in PBP. Extensive experiments on different data sets demonstrate that CE-PBP achieves a higher topic modeling accuracy and reduces more than 80% communication cost than the state-of-the-art parallel Gibbs sampling (PGS) 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"
    - "online belief propagation for topic modeling"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "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 "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"
    - "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:
    - "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"
    - "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-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"
    - "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"
    - "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"
    - "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"
    - "residual belief propagation for topic modeling"

Connected through hyperedge HE-6 (induced by source paper "residual belief propagation for topic modeling"):
  with members:
    - "learning topic models by belief propagation"
    - "online belief propagation for topic modeling"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "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)