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 "inference and evaluation of the multinomial mixture model for text clustering"):
  Abstract: In this article, we investigate the use of a probabilistic model for unsupervised clustering in text collections. Unsupervised clustering has become a basic module for many intelligent text processing applications, such as information retrieval, text classification or information extraction. Recent proposals have been made of probabilistic clustering models, which build ''soft'' theme-document associations. These models allow to compute, for each document, a probability vector whose values can be interpreted as the strength of the association between documents and clusters. As such, these vectors can also serve to project texts into a lower-dimensional ''semantic'' space. These models however pose non-trivial estimation problems, which are aggravated by the very high dimensionality of the parameter space. The model considered in this paper consists of a mixture of multinomial distributions over the word counts, each component corresponding to a different theme. We propose a systematic evaluation framework to contrast various estimation procedures for this model. Starting with the expectation-maximization (EM) algorithm as the basic tool for inference, we discuss the importance of initialization and the influence of other features, such as the smoothing strategy or the size of the vocabulary, thereby illustrating the difficulties incurred by the high dimensionality of the parameter space. We empirically show that, in the case of text processing, these difficulties can be alleviated by introducing the vocabulary incrementally, due to the specific profile of the word count distributions. Using the fact that the model parameters can be analytically integrated out, we finally show that Gibbs sampling on the theme configurations is tractable and compares favorably to the basic EM approach.

Includes members:
  Member "applying discrete pca in data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vsec lda boosting topic modeling with embedded vocabulary selection")
  Member "machine learning in automated text categorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences")

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.IR(Information Retrieval)