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: "rethinking collapsed variational bayes inference for lda"
  Abstract: We propose a novel interpretation of the collapsed variational Bayes inference with a zero-order Taylor expansion approximation, called CVB0 inference, for latent Dirichlet allocation (LDA). We clarify the properties of the CVB0 inference by using the alpha-divergence. We show that the CVB0 inference is composed of two different divergence projections: alpha=1 and -1. This interpretation will help shed light on CVB0 works.

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"
    - "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"
    - "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 "collapsed variational bayes inference of infinite relational model"):
  with members:
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "community detection in networks with node attributes"
    - "on smoothing and inference for topic models"
    - "fast variational inference in the conjugate exponential family"
    - "an infinite latent attribute model for network data"

Connected through hyperedge HE-4 (induced by source paper "stochastic collapsed variational bayesian inference for latent dirichlet allocation"):
  with members:
    - "reconstructing pompeian households"
    - "on smoothing and inference for topic models"
    - "julia a fast dynamic language for technical computing"
    - "sparse stochastic inference for latent dirichlet allocation"

Connected through hyperedge HE-5 (induced by source paper "scalable recommendation with poisson factorization"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"
    - "applying discrete pca in data analysis"
    - "efficient and principled method for detecting communities in networks"
    - "feature hashing for large scale multitask learning"

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)