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: "how many topics stability analysis for topic models"
  Abstract: Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic. Despite the diversity of topic modeling algorithms that have been proposed, a common challenge in successfully applying these techniques is the selection of an appropriate number of topics for a given corpus. Choosing too few topics will produce results that are overly broad, while choosing too many will result in the "over-clustering" of a corpus into many small, highly-similar topics. In this paper, we propose a term-centric stability analysis strategy to address this issue, the idea being that a model with an appropriate number of topics will be more robust to perturbations in the data. Using a topic modeling approach based on matrix factorization, evaluations performed on a range of corpora show that this strategy can successfully guide the model selection process.

Connected through hyperedge HE-1 (induced by source paper "improving reliability of latent dirichlet allocation by assessing its stability using clustering techniques on replicated runs"):
  with members:
    - "measuring lda topic stability from clusters of replicated runs"

Connected through hyperedge HE-2 (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"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "sparse stochastic inference for latent dirichlet allocation"

Connected through hyperedge HE-3 (induced by source paper "measuring lda topic stability from clusters of replicated runs"):
  with members:
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "bootstrapping a lexicon for emotional arousal in software engineering"

Connected through hyperedge HE-4 (induced by source paper "topic diffusion discovery based on sparseness constrained non negative matrix factorization"):
  with members:
    - "learning topic models going beyond svd"

Connected through hyperedge HE-5 (induced by source paper "understanding news story chains using information retrieval and network clustering techniques"):
  with members:
    - "integrating the probabilistic models bm25 bm25f into lucene"

Connected through hyperedge HE-6 (induced by source paper "stability of topic modeling via matrix factorization"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "deterministic initialization of the k means algorithm using hierarchical clustering"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-7 (induced by source paper "analysis of computational science papers from iccs 2001 2016 using topic modeling and graph theory"):
  with members:
    - "exploring the political agenda of the european parliament using a dynamic topic modeling approach"
    - "continuous time dynamic topic models"

Connected through hyperedge HE-8 (induced by source paper "exploring the political agenda of the european parliament using a dynamic topic modeling approach"):
  with members:
    - "efficient estimation of word representations in vector space"

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)