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 "a new approach to speeding up topic modeling"):
  Abstract: Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic space. To process massive corpora having a large number of topics, the training iteration of batch LDA algorithms is often inefficient and time-consuming. To accelerate the training speed, ABP actively scans the subset of corpus and searches the subset of topic space for topic modeling, therefore saves enormous training time in each iteration. To ensure accuracy, ABP selects only those documents and topics that contribute to the largest residuals within the residual belief propagation (RBP) framework. On four real-world corpora, ABP performs around $10$ to $100$ times faster than state-of-the-art batch LDA algorithms with a comparable topic modeling accuracy.

Includes members:
  Member "learning topic models by belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast online em for big topic modeling")
  Member "residual belief propagation informed scheduling for asynchronous message passing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "memory efficient topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a topic modeling toolbox using belief propagation")
  Member "communication efficient parallel belief propagation for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "topic grouper an agglomerative clustering approach to topic modeling")
  Member "a topic modeling toolbox using belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heron inference for bayesian graphical models")
  Member "sparse stochastic inference for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "topic discovery in massive text corpora based on min hashing")
  Member "residual belief propagation for topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.LG(Machine Learning)