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 "topic discovery in massive text corpora based on min hashing"):
  Abstract: Abstract   Topics have proved to be a valuable source of information for exploring, discovering, searching and representing the contents of text corpora. They have also been useful for different natural language processing tasks such as text classification, text summarization and machine translation. Most existing topic discovery approaches require the number of topics to be provided beforehand. However, an appropriate number of topics for a given corpus depends on its characteristics and is often difficult to estimate. In addition, in order to handle massive amounts of text documents, the vocabulary must be reduced considerably and large computer clusters and/or GPUs are typically required. This paper describes Sampled Min-Hashing (SMH), a scalable approach to topic discovery which does not require the number of topics to be specified in advance and can handle massive text corpora and large vocabularies using modest computer resources. The basic idea behind SMH is to generate multiple random partitions of the corpus vocabulary to find sets of highly co-occurring words, which are then clustered to produce the final topics. An extensive qualitative and quantitative evaluation on the 20 Newsgroups, Reuters, Spanish Wikipedia and English Wikipedia corpora shows that SMH is able to consistently discover meaningful and coherent topics at scale. Remarkably, the time required by SMH grows linearly with the size of the corpus and the number of words in the vocabulary; a non-parallel implementation of SMH was able to discover topics from the whole English version of Wikipedia (5M documents approximately) with a vocabulary of 1M words in less than 7 h. Our findings provide further evidence of the relevance and generality of beyond-pairwise co-occurrences for pattern discovery on large-scale discrete data, which opens the door for other applications and several interesting research directions.

Includes members:
  Member "saberlda sparsity aware learning of topic models on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "culda_cgs solving large scale lda problems on gpus")
  Member "sampled weighted min hashing for large scale topic mining":
    also connected through other hyperedges:
      (none)
  Member "a scalable asynchronous distributed algorithm for topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advances in collaborative filtering and ranking")
  Member "consistent weighted sampling made fast small and easy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a memory efficient sketch method for estimating high similarities in streaming sets")
  Member "how many topics stability analysis for topic models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving reliability of latent dirichlet allocation by assessing its stability using clustering techniques on replicated runs")
  Member "stochastic collapsed variational bayesian inference for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a new anchor word selection method for the separable topic discovery")
  Member "sparse stochastic inference for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient correlated topic modeling with topic embedding")

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.CL(Computation and Language)