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: "on smoothing and inference for topic models"
  Abstract: Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and this variety motivates the need for careful empirical comparisons. In this paper, we highlight the close connections between these approaches. We find that the main differences are attributable to the amount of smoothing applied to the counts. When the hyperparameters are optimized, the differences in performance among the algorithms diminish significantly. The ability of these algorithms to achieve solutions of comparable accuracy gives us the freedom to select computationally efficient approaches. Using the insights gained from this comparative study, we show how accurate topic models can be learned in several seconds on text corpora with thousands of documents.

Connected through hyperedge HE-1 (induced by source paper "topic grouper an agglomerative clustering approach to topic modeling"):
  with members:
    - "probabilistic latent semantic analysis"
    - "model based hierarchical clustering"

Connected through hyperedge HE-2 (induced by source paper "a coefficient of determination for probabilistic topic models"):
  with members:
    - "evaluating topic coherence measures"

Connected through hyperedge HE-3 (induced by source paper "atm adversarial neural topic model"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "probabilistic fasttext for multi sense word embeddings"
    - "toward controlled generation of text"
    - "adversarial ranking for language generation"
    - "improved training of wasserstein gans"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-4 (induced by source paper "what is wrong with topic modeling and how to fix it using search based software engineering"):
  with members:
    - "easy over hard a case study on deep learning"
    - "tuning for software analytics"
    - "mllib machine learning in apache spark"
    - "expectation propogation for the generative aspect model"

Connected through hyperedge HE-5 (induced by source paper "towards training probabilistic topic models on neuromorphic multi chip systems"):
  with members:
    - "fast latent variable models for inference and visualization on mobile devices"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "prior aware dual decomposition document specific topic inference for spectral topic models"):
  with members:
    - "provable algorithms for inference in topic models"
    - "spectral methods for correlated topic models"
    - "robust spectral inference for joint stochastic matrix factorization"
    - "probabilistic latent semantic analysis"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "a spectral algorithm for latent dirichlet allocation"

Connected through hyperedge HE-7 (induced by source paper "rookie a unique approach for exploring news archives"):
  with members:
    - "a neural attention model for abstractive sentence summarization"
    - "posterior calibration and exploratory analysis for natural language processing models"

Connected through hyperedge HE-8 (induced by source paper "unsupervised terminological ontology learning based on hierarchical topic modeling"):
  with members:
    - "neighborhood mixture model for knowledge base completion"

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)