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: "linguistic diversities of demographic groups in twitter"
  Abstract: The massive popularity of online social media provides a unique opportunity for researchers to study the linguistic characteristics and patterns of user's interactions. In this paper, we provide an in-depth characterization of language usage across demographic groups in Twitter. In particular, we extract the gender and race of Twitter users located in the U.S. using advanced image processing algorithms from Face++. Then, we investigate how demographic groups (i.e. male/female, Asian/Black/White) differ in terms of linguistic styles and also their interests. We extract linguistic features from 6 categories (affective attributes, cognitive attributes, lexical density and awareness, temporal references, social and personal concerns, and interpersonal focus), in order to identify the similarities and differences in particular writing set of attributes. In addition, we extract the absolute ranking difference of top phrases between demographic groups. As a dimension of diversity, we also use the topics of interest that we retrieve from each user. Our analysis unveils clear differences in the writing styles (and the topics of interest) of different demographic groups, with variation seen across both gender and race lines. We hope our effort can stimulate the development of new studies related to demographic information in the online space.

Connected through hyperedge HE-1 (induced by source paper "white man and highly followed gender and race inequalities in twitter"):
  with members:
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "demographics of news sharing in the u s twittersphere"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "from migration corridors to clusters the value of google data for migration studies"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "gendered conversation in a social game streaming platform"

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)