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: "twitter sentiment analysis lexicon method machine learning method and their combination"
  Abstract: This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combinations and show that enhancing sentiment lexicons with emoticons, abbreviations and social-media slang expressions increases the accuracy of lexicon-based classification for Twitter. We discuss the importance of feature generation and feature selection processes for machine learning sentiment classification. To quantify the performance of the main sentiment analysis methods over Twitter we run these algorithms on a benchmark Twitter dataset from the SemEval-2013 competition, task 2-B. The results show that machine learning method based on SVM and Naive Bayes classifiers outperforms the lexicon method. We present a new ensemble method that uses a lexicon based sentiment score as input feature for the machine learning approach. The combined method proved to produce more precise classifications. We also show that employing a cost-sensitive classifier for highly unbalanced datasets yields an improvement of sentiment classification performance up to 7%.

Connected through hyperedge HE-1 (induced by source paper "a new statistical approach for comparing algorithms for lexicon based sentiment analysis"):
  with members:
    - "multilingual twitter sentiment classification the role of human annotators"

Connected through hyperedge HE-2 (induced by source paper "multilingual twitter sentiment classification the role of human annotators"):
  with members:
    - "emotional dynamics in the age of misinformation"
    - "sentiment uncertainty and spam in twitter streams and its implications for general purpose realtime sentiment analysis"
    - "the effects of twitter sentiment on stock price returns"
    - "sentiment of emojis"

Connected through hyperedge HE-3 (induced by source paper "sentiment enhanced multidimensional analysis of online social networks perception of the mediterranean refugees crisis"):
  with members:
    - "polarized user and topic tracking in twitter"
    - "quantifying controversy in social media"
    - "geo located twitter as proxy for global mobility patterns"
    - "the geospatial characteristics of a social movement communication network"

Connected through hyperedge HE-4 (induced by source paper "the effects of twitter sentiment on stock price returns"):
  with members:
    - "emotional dynamics in the age of misinformation"
    - "twitter sentiment analysis applied to finance a case study in the retail industry"
    - "cohesiveness in financial news and its relation to market volatility"
    - "where in the world are you geolocation and language identification in twitter"
    - "the royal birth of 2013 analysing and visualising public sentiment in the uk using twitter"
    - "dynamical classes of collective attention in twitter"
    - "twitter mood predicts the stock market"

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.CL(Computation and Language)