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 "towards big topic modeling"):
  Abstract: To solve the big topic modeling problem, we need to reduce both time and space complexities of batch latent Dirichlet allocation (LDA) algorithms. Although parallel LDA algorithms on the multi-processor architecture have low time and space complexities, their communication costs among processors often scale linearly with the vocabulary size and the number of topics, leading to a serious scalability problem. To reduce the communication complexity among processors for a better scalability, we propose a novel communication-efficient parallel topic modeling architecture based on power law, which consumes orders of magnitude less communication time when the number of topics is large. We combine the proposed communication-efficient parallel architecture with the online belief propagation (OBP) algorithm referred to as POBP for big topic modeling tasks. Extensive empirical results confirm that POBP has the following advantages to solve the big topic modeling problem: 1) high accuracy, 2) communication-efficient, 3) fast speed, and 4) constant memory usage when compared with recent state-of-the-art parallel LDA algorithms on the multi-processor architecture.

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 "learning topic models by belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast online em for big topic modeling")
  Member "a practical algorithm for topic modeling with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training")
  Member "residual belief propagation informed scheduling for asynchronous message passing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  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")
  Member "a topic modeling toolbox using belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "heron inference for bayesian graphical models")

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.LG(Machine Learning)