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: "a scalable asynchronous distributed algorithm for topic modeling"
  Abstract: Learning meaningful topic models with massive document collections which contain millions of documents and billions of tokens is challenging because of two reasons: First, one needs to deal with a large number of topics (typically in the order of thousands). Second, one needs a scalable and efficient way of distributing the computation across multiple machines. In this paper we present a novel algorithm F+Nomad LDA which simultaneously tackles both these problems. In order to handle large number of topics we use an appropriately modified Fenwick tree. This data structure allows us to sample from a multinomial distribution over $T$ items in $O(\log T)$ time. Moreover, when topic counts change the data structure can be updated in $O(\log T)$ time. In order to distribute the computation across multiple processor we present a novel asynchronous framework inspired by the Nomad algorithm of \cite{YunYuHsietal13}. We show that F+Nomad LDA significantly outperform state-of-the-art on massive problems which involve millions of documents, billions of words, and thousands of topics.

Connected through hyperedge HE-1 (induced by source paper "advances in collaborative filtering and ranking"):
  with members:
    - "temporal collaborative ranking via personalized transformer"
    - "graph dna deep neighborhood aware graph encoding for collaborative filtering"
    - "stochastic shared embeddings data driven regularization of embedding layers"
    - "hierarchical gating networks for sequential recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-2 (induced by source paper "topic discovery in massive text corpora based on min hashing"):
  with members:
    - "saberlda sparsity aware learning of topic models on gpus"
    - "sampled weighted min hashing for large scale topic mining"
    - "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-3 (induced by source paper "online machine learning in big data streams"):
  with members:
    - "computing web scale topic models using an asynchronous parameter server"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "clipper a low latency online prediction serving system"
    - "xgboost a scalable tree boosting system"
    - "general factorization framework for context aware recommendations"
    - "vht vertical hoeffding tree"
    - "evaluation of recommender systems in streaming environments"
    - "optimal and adaptive algorithms for online boosting"

Connected through hyperedge HE-4 (induced by source paper "fast estimation of causal interactions using wold processes"):
  with members:
    - "network inference via the time varying graphical lasso"
    - "motifs in temporal networks"
    - "learning network of multivariate hawkes processes a time series approach"
    - "learning granger causality for hawkes processes"
    - "microscopic approach of a time elapsed neural model"

Connected through hyperedge HE-5 (induced by source paper "culda_cgs solving large scale lda problems on gpus"):
  with members:
    - "efficient correlated topic modeling with topic embedding"
    - "saberlda sparsity aware learning of topic models on gpus"
    - "node2vec scalable feature learning for networks"
    - "gunrock a high performance graph processing library on the gpu"
    - "stochastic collapsed variational bayesian inference for latent dirichlet allocation"

Connected through hyperedge HE-6 (induced by source paper "heron inference for bayesian graphical models"):
  with members:
    - "same but different fast and high quality gibbs parameter estimation"
    - "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-7 (induced by source paper "computing web scale topic models using an asynchronous parameter server"):
  with members:
    - "on smoothing and inference for topic models"

Connected through hyperedge HE-8 (induced by source paper "saberlda sparsity aware learning of topic models on gpus"):
  with members:
    - "big learning with bayesian methods"
    - "same but different fast and high quality gibbs parameter estimation"
    - "discriminative relational topic models"

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)