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 "latent dirichlet allocation lda and topic modeling models applications a survey"):
  Abstract: Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data, text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modeling, which Latent Dirichlet allocation (LDA) is one of the most popular methods in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper can be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated scholarly articles highly (between 2003 to 2016) related to Topic Modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. Also, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

Includes members:
  Member "improving topic models with latent feature word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "catching fire via likes inferring topic preferences of trump followers on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "catching up with trends the changing landscape of political discussions on twitter in 2014 and 2019")
  Member "fast online em for big topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "peacock learning long tail topic features for industrial applications")
  Member "sub story detection in twitter with hierarchical dirichlet processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate")
  Member "unveiling the political agenda of the european parliament plenary a topical analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on computational politics")
  Member "et lda joint topic modeling for aligning events and their twitter feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "event detection and retrieval on social media")
  Member "expectation propogation for the generative aspect model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving genomics based predictions for precision medicine through active elicitation of expert knowledge")
  Member "the author topic model for authors and documents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discriminative topic mining via category name guided text embedding")

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)