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: "the readability of tweets and their geographic correlation with education"
  Abstract: Twitter has rapidly emerged as one of the largest worldwide venues for written communication. Thanks to the ease with which vast quantities of tweets can be mined, Twitter has also become a source for studying modern linguistic style. The readability of text has long provided a simple method to characterize the complexity of language and ease that documents may be understood by readers. In this note we use a modified version of the Flesch Reading Ease formula, applied to a corpus of 17.4 million tweets. We find tweets have characteristically more difficult readability scores compared to other short format communication, such as SMS or chat. This linguistic difference is insensitive to the presence of "hashtags" within tweets. By utilizing geographic data provided by 2% of users, joined with "ZIP Code Tabulation Area" (ZCTA) level education data from the U.S. Census, we find an intriguing correlation between the average readability and the college graduation rate within a ZCTA. This points towards a difference in either the underlying language, or a change in the type of content being tweeted in these areas

Connected through hyperedge HE-1 (induced by source paper "you write like you eat stylistic variation as a predictor of social stratification"):
  with members:
    - "bleaching text abstract features for cross lingual gender prediction"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "understanding and overcoming biases in customer reviews"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "distant supervision for emotion detection using facebook reactions"
    - "learning when to trust distant supervision an application to low resource pos tagging using cross lingual projection"
    - "computational sociolinguistics a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "predicting the relative difficulty of single sentences with and without surrounding context"):
  with members:
    - "scikit learn machine learning in python"

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.SI(Social and Information Networks)