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: "a topic modeling toolbox using belief propagation"
  Abstract: Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interests and touches on many important applications in text mining, computer vision and computational biology. This paper introduces a topic modeling toolbox (TMBP) based on the belief propagation (BP) algorithms. TMBP toolbox is implemented by MEX C++/Matlab/Octave for either Windows 7 or Linux. Compared with existing topic modeling packages, the novelty of this toolbox lies in the BP algorithms for learning LDA-based topic models. The current version includes BP algorithms for latent Dirichlet allocation (LDA), author-topic models (ATM), relational topic models (RTM), and labeled LDA (LaLDA). This toolbox is an ongoing project and more BP-based algorithms for various topic models will be added in the near future. Interested users may also extend BP algorithms for learning more complicated topic models. The source codes are freely available under the GNU General Public Licence, Version 1.0 at this https URL

Connected through hyperedge HE-1 (induced by source paper "heron inference for bayesian graphical models"):
  with members:
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "same but different fast and high quality gibbs parameter estimation"
    - "expectation propogation for the generative aspect model"
    - "rethinking collapsed variational bayes inference for lda"

Connected through hyperedge HE-2 (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-3 (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-4 (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"
    - "sparse stochastic inference for latent dirichlet allocation"

Connected through hyperedge HE-5 (induced by source paper "learning topic models by belief propagation"):
  with members:
    - "on smoothing and inference for topic models"
    - "the author topic model for authors and documents"
    - "using variational inference and mapreduce to scale topic modeling"
    - "higher order markov tag topic models for tagged documents and images"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "sparse stochastic inference for latent dirichlet allocation"
    - "residual belief propagation for topic modeling"

Connected through hyperedge HE-8 (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"
    - "communication efficient parallel belief propagation for latent dirichlet allocation"
    - "on smoothing and inference for topic models"

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)