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: "artex is another text summarizer"
  Abstract: This paper describes Artex, another algorithm for Automatic Text Summarization. In order to rank sentences, a simple inner product is calculated between each sentence, a document vector (text topic) and a lexical vector (vocabulary used by a sentence). Summaries are then generated by assembling the highest ranked sentences. No ruled-based linguistic post-processing is necessary in order to obtain summaries. Tests over several datasets (coming from Document Understanding Conferences (DUC), Text Analysis Conferences (TAC), evaluation campaigns, etc.) in French, English and Spanish have shown that summarizer achieves interesting results.

Connected through hyperedge HE-1 (induced by source paper "intweetive text summarization"):
  with members:
    - "towards real time summarization of scheduled events from twitter streams"
    - "automatic summarization system coupled with a question answering system qaas"

Connected through hyperedge HE-2 (induced by source paper "automatic text summarization approaches to speed up topic model learning process"):
  with members:
    - "probabilistic latent semantic analysis"
    - "probabilistic models for unified collaborative and content based recommendation in sparse data environments"
    - "expectation propogation for the generative aspect model"
    - "the author topic model for authors and documents"

Connected through hyperedge HE-3 (induced by source paper "a review of features for the discrimination of twitter users application to the prediction of offline influence"):
  with members:
    - "temporal multinomial mixture for instance oriented evolutionary clustering"
    - "klout score measuring influence across multiple social networks"
    - "detecting real world influence through twitter"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "identifying the community roles of social capitalists in the twitter network"
    - "understanding types of users on twitter"
    - "i wanted to predict elections with twitter and all i got was this lousy paper a balanced survey on election prediction using twitter data"
    - "comparative evaluation of community detection algorithms a topological approach"

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.IR(Information Retrieval)