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: "saberlda sparsity aware learning of topic models on gpus"
  Abstract: Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete count data such as text and images. Applications require LDA to handle both large datasets and a large number of topics. Though distributed CPU systems have been used, GPU-based systems have emerged as a promising alternative because of the high computational power and memory bandwidth of GPUs. However, existing GPU-based LDA systems cannot support a large number of topics because they use algorithms on dense data structures whose time and space complexity is linear to the number of topics. In this paper, we propose SaberLDA, a GPU-based LDA system that implements a sparsity-aware algorithm to achieve sublinear time complexity and scales well to learn a large number of topics. To address the challenges introduced by sparsity, we propose a novel data layout, a new warp-based sampling kernel, and an efficient sparse count matrix updating algorithm that improves locality, makes efficient utilization of GPU warps, and reduces memory consumption. Experiments show that SaberLDA can learn from billions-token-scale data with up to 10,000 topics, which is almost two orders of magnitude larger than that of the previous GPU-based systems. With a single GPU card, SaberLDA is able to learn 10,000 topics from a dataset of billions of tokens in a few hours, which is only achievable with clusters with tens of machines before.

Connected through hyperedge HE-1 (induced by source paper "topic discovery in massive text corpora based on min hashing"):
  with members:
    - "sampled weighted min hashing for large scale topic mining"
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "consistent weighted sampling made fast small and easy"
    - "how many topics stability analysis for topic models"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"
    - "sparse stochastic inference for latent dirichlet allocation"

Connected through hyperedge HE-2 (induced by source paper "culda_cgs solving large scale lda problems on gpus"):
  with members:
    - "efficient correlated topic modeling with topic embedding"
    - "node2vec scalable feature learning for networks"
    - "gunrock a high performance graph processing library on the gpu"
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"

Connected through hyperedge HE-3 (induced by source paper "origami a heterogeneous split architecture for in memory acceleration of learning"):
  with members:
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "big learning with bayesian methods"):
  with members:
    - "max margin nonparametric latent feature models for link prediction"
    - "theano a python framework for fast computation of mathematical expressions"
    - "model parallel inference for big topic models"
    - "neural variational inference and learning in belief networks"
    - "exploiting the statistics of learning and inference"
    - "caffe convolutional architecture for fast feature embedding"
    - "peacock learning long tail topic features for industrial applications"
    - "efficient gradient based inference through transformations between bayes nets and neural nets"

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.DC(Distributed, Parallel, and Cluster Computing)