human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "same but different fast and high quality gibbs parameter estimation"
  Abstract: Gibbs sampling is a workhorse for Bayesian inference but has several limitations when used for parameter estimation, and is often much slower than non-sampling inference methods. SAME (State Augmentation for Marginal Estimation) \cite{Doucet99,Doucet02} is an approach to MAP parameter estimation which gives improved parameter estimates over direct Gibbs sampling. SAME can be viewed as cooling the posterior parameter distribution and allows annealed search for the MAP parameters, often yielding very high quality (lower loss) estimates. But it does so at the expense of additional samples per iteration and generally slower performance. On the other hand, SAME dramatically increases the parallelism in the sampling schedule, and is an excellent match for modern (SIMD) hardware. In this paper we explore the application of SAME to graphical model inference on modern hardware. We show that combining SAME with factored sample representation (or approximation) gives throughput competitive with the fastest symbolic methods, but with potentially better quality. We describe experiments on Latent Dirichlet Allocation, achieving speeds similar to the fastest reported methods (online Variational Bayes) and lower cross-validated loss than other LDA implementations. The method is simple to implement and should be applicable to many other models.

Connected through hyperedge HE-1 (induced by source paper "heron inference for bayesian graphical models"):
  with members:
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "expectation propogation for the generative aspect model"
    - "a topic modeling toolbox using belief propagation"
    - "rethinking collapsed variational bayes inference for lda"

Connected through hyperedge HE-2 (induced by source paper "saberlda sparsity aware learning of topic models on gpus"):
  with members:
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "big learning with bayesian methods"
    - "discriminative relational topic models"

Connected through hyperedge HE-3 (induced by source paper "online but accurate inference for latent variable models with local gibbs sampling"):
  with members:
    - "streaming gibbs sampling for lda model"
    - "structured stochastic variational inference"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"

Connected through hyperedge HE-4 (induced by source paper "splash user friendly programming interface for parallelizing stochastic algorithms"):
  with members:
    - "mllib machine learning in apache spark"
    - "communication efficient distributed dual coordinate ascent"
    - "expectation propagation for approximate bayesian inference"
    - "mli an api for distributed machine learning"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "online importance weight aware updates"

Question: Please tell me which class the center node 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)