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: "progressive em for latent tree models and hierarchical topic detection"
  Abstract: Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition, topic-document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarchies. However, HLTA relies on the Expectation-Maximization (EM) algorithm for parameter estimation and hence is not efficient enough to deal with large datasets. In this paper, we propose a method to drastically speed up HLTA using a technique inspired by recent advances in the moments method. Empirical experiments show that our method greatly improves the efficiency of HLTA. It is as efficient as the state-of-the-art LDA-based method for hierarchical topic detection and finds substantially better topics and topic hierarchies.

Connected through hyperedge HE-1 (induced by source paper "conformative filtering for implicit feedback data"):
  with members:
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "latent tree analysis"
    - "image based recommendations on styles and substitutes"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "learning sparse deep feedforward networks via tree skeleton expansion"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "neural architecture search with reinforcement learning"
    - "sparse boltzmann machines with structure learning as applied to text analysis"
    - "learning both weights and connections for efficient neural networks"
    - "data free parameter pruning for deep neural networks"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "learning hierarchical item categories from implicit feedback data for efficient recommendations and browsing"):
  with members:
    - "using taste groups for collaborative filtering"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "bayesian rose trees"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-4 (induced by source paper "a novel document generation process for topic detection based on hierarchical latent tree models"):
  with members:
    - "sparse boltzmann machines with structure learning as applied to text analysis"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "anchored correlation explanation topic modeling with minimal domain knowledge"):
  with members:
    - "discovering structure in high dimensional data through correlation explanation"
    - "probabilistic latent semantic analysis"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "scikit learn machine learning in python"
    - "the author topic model for authors and documents"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-6 (induced by source paper "latent tree analysis"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "identification and classification of tcm syndrome types among patients with vascular mild cognitive impairment using latent tree analysis"
    - "sparse boltzmann machines with structure learning as applied to text analysis"
    - "learning both weights and connections for efficient neural networks"
    - "a survey on latent tree models and applications"
    - "latent tree models and approximate inference in bayesian networks"

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)