human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "scalable dynamic topic modeling with clustered latent dirichlet allocation clda"):
  Abstract: Topic modeling is an increasingly important component of Big Data analytics, enabling the sense-making of highly dynamic and diverse streams of text data. Traditional methods such as Dynamic Topic Modeling (DTM), while mathematically elegant, do not lend themselves well to direct parallelization because of dependencies from one time step to another. Data decomposition approaches that partition data across time segments and then combine results in a global view of the dynamic change of topics enable execution of topic models on much larger datasets than is possibly without data decomposition. However, these methods are difficult to analyze mathematically and are relatively untested for quality of topics and performance on parallel systems. In this paper, we introduce and empirically analyze Clustered Latent Dirichlet Allocation (CLDA), a method for extracting dynamic latent topics from a collection of documents. CLDA uses a data decomposition strategy to partition data. CLDA takes advantage of parallelism, enabling fast execution for even very large datasets and a large number of topics. A large corpus is split into local segments to extract textual information from different time steps. Latent Dirichlet Allocation (LDA) is applied to infer topics at local segments. The results are merged, and clustering is used to combine topics from different segments into global topics. Results show that the perplexity is comparable and that topics generated by this algorithm are similar to those generated by DTM. In addition, CLDA is two orders of magnitude faster than existing approaches and allows for more freedom of experiment design. In this paper CLDA is applied successfully to seventeen years of NIPS conference papers, seventeen years of computer science journal abstracts, and to forty years of the PubMed corpus.

Includes members:
  Member "moliere automatic biomedical hypothesis generation system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cbag conditional biomedical abstract generation")
  Member "bibliographic analysis with the citation network topic model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attributed social network embedding")
  Member "peacock learning long tail topic features for industrial applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient correlated topic modeling with topic embedding")
  Member "dependent hierarchical normalized random measures for dynamic topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic infinite mixed membership stochastic blockmodel")
  Member "timeline a dynamic hierarchical dirichlet process model for recovering birth death and evolution of topics in text stream":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "homogeneity based transmissive process to model true and false news in social networks")

Question: Please tell me which class the source paper 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.IR(Information Retrieval)