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: "sparse stochastic inference for latent dirichlet allocation"
  Abstract: We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models.

Connected through hyperedge HE-1 (induced by source paper "topic discovery in massive text corpora based on min hashing"):
  with members:
    - "saberlda sparsity aware learning of topic models on gpus"
    - "sampled weighted min hashing for large scale topic mining"
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "consistent weighted sampling made fast small and easy"
    - "how many topics stability analysis for topic models"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"

Connected through hyperedge HE-2 (induced by source paper "efficient correlated topic modeling with topic embedding"):
  with members:
    - "on unifying deep generative models"
    - "nonparametric variational auto encoders for hierarchical representation learning"
    - "recurrent topic transition gan for visual paragraph generation"
    - "node2vec scalable feature learning for networks"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "nonparametric spherical topic modeling with word embeddings"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "peacock learning long tail topic features for industrial applications"

Connected through hyperedge HE-3 (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-4 (induced by source paper "online bayesian collaborative topic regression"):
  with members:
    - "collaborative deep learning for recommender systems"
    - "online bayesian passive aggressive learning"
    - "collaborative topic regression with social matrix factorization for recommendation systems"

Connected through hyperedge HE-5 (induced by source paper "provable bayesian inference via particle mirror descent"):
  with members:
    - "gaussian processes for big data"
    - "nonparametric variational inference"
    - "kernel belief propagation"

Connected through hyperedge HE-6 (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-7 (induced by source paper "scalable and robust construction of topical hierarchies"):
  with members:
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for latent dirichlet allocation"

Connected through hyperedge HE-8 (induced by source paper "structured stochastic variational inference"):
  with members:
    - "stochastic collapsed variational bayesian inference 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)