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 "peacock learning long tail topic features for industrial applications"):
  Abstract: Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems. A main underlying reason is that the topic models used have been too small in scale to be useful; for example, some of the largest LDA models reported in literature have up to $10^3$ topics, which cover difficultly the long-tail semantic word sets. In this paper, we show that the number of topics is a key factor that can significantly boost the utility of topic-modeling systems. In particular, we show that a "big" LDA model with at least $10^5$ topics inferred from $10^9$ search queries can achieve a significant improvement on industrial search engine and online advertising systems, both of which serving hundreds of millions of users. We develop a novel distributed system called Peacock to learn big LDA models from big data. The main features of Peacock include hierarchical distributed architecture, real-time prediction and topic de-duplication. We empirically demonstrate that the Peacock system is capable of providing significant benefits via highly scalable LDA topic models for several industrial applications.

Includes members:
  Member "fast online em for big topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent dirichlet allocation lda and topic modeling models applications a survey")
  Member "stochastic collapsed variational bayesian inference for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topic discovery in massive text corpora based on min hashing")
  Member "towards big topic modeling":
    also connected through other hyperedges:
      (none)
  Member "learning topic models by belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast online em for big topic modeling")
  Member "expectation propogation for the generative aspect model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving genomics based predictions for precision medicine through active elicitation of expert knowledge")
  Member "communication efficient parallel belief propagation for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast online em for big topic modeling")
  Member "on smoothing and inference for topic models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "topic grouper an agglomerative clustering approach to topic modeling")
  Member "a new approach to speeding up topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast online em for big topic modeling")

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)