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 "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.

Includes members:
  Member "demographics of news sharing in the u s twittersphere":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gender and racial diversity in commercial brands advertising images on social media")
  Member "why do men get more attention exploring factors behind success in an online design community":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the effects of gender signals and performance in online product reviews")
  Member "who makes trends understanding demographic biases in crowdsourced recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairface face attribute dataset for balanced race gender and age")
  Member "greysanatomy vs yankees demographics and hashtag use on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "whose tweets are surveilled for the police an audit of social media monitoring tool via log files")
  Member "inferring gender from names on the web a comparative evaluation of gender detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the effects of gender signals and performance in online product reviews")
  Member "reverse engineering socialbot infiltration strategies in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the mass fake news and cognition security")
  Member "learning deep face representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quadruplet network with one shot learning for fast visual object tracking")

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.SI(Social and Information Networks)