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: "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
  Abstract: In the internet era there has been an explosion in the amount of digital text information available, leading to difficulties of scale for traditional inference algorithms for topic models. Recent advances in stochastic variational inference algorithms for latent Dirichlet allocation (LDA) have made it feasible to learn topic models on large-scale corpora, but these methods do not currently take full advantage of the collapsed representation of the model. We propose a stochastic algorithm for collapsed variational Bayesian inference for LDA, which is simpler and more efficient than the state of the art method. We show connections between collapsed variational Bayesian inference and MAP estimation for LDA, and leverage these connections to prove convergence properties of the proposed algorithm. In experiments on large-scale text corpora, the algorithm was found to converge faster and often to a better solution than the previous method. Human-subject experiments also demonstrated that the method can learn coherent topics in seconds on small corpora, facilitating the use of topic models in interactive document analysis software.

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"
    - "sparse stochastic inference for latent dirichlet allocation"

Connected through hyperedge HE-2 (induced by source paper "a new anchor word selection method for the separable topic discovery"):
  with members:
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "topic modeling via full dependence mixtures"):
  with members:
    - "adam a method for stochastic optimization"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-4 (induced by source paper "culda_cgs solving large scale lda problems on gpus"):
  with members:
    - "efficient correlated topic modeling with topic embedding"
    - "saberlda sparsity aware learning of topic models on gpus"
    - "node2vec scalable feature learning for networks"
    - "gunrock a high performance graph processing library on the gpu"
    - "a scalable asynchronous distributed algorithm for topic modeling"

Connected through hyperedge HE-5 (induced by source paper "collapsed variational inference for nonparametric bayesian group factor analysis"):
  with members:
    - "sparse group factor analysis for biclustering of multiple data sources"
    - "a slice sampler for restricted hierarchical beta process with applications to shared subspace learning"
    - "the infinite hierarchical factor regression model"

Connected through hyperedge HE-6 (induced by source paper "measuring motivations of crowdworkers the multidimensional crowdworker motivation scale"):
  with members:
    - "characterizing the global crowd workforce a cross country comparison of crowdworker demographics"
    - "the polylingual labeled topic model"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-7 (induced by source paper "truncation free hybrid inference for dpmm"):
  with members:
    - "practical collapsed stochastic variational inference for the hdp"

Connected through hyperedge HE-8 (induced by source paper "fast online em for big topic modeling"):
  with members:
    - "peacock learning long tail topic features for industrial applications"
    - "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"
    - "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)