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 "text mining forma mentis networks reconstruct public perception of the stem gender gap in social media"):
  Abstract: Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e. the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting, representing and understanding mindsets' structure, in Latin "forma mentis", from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts, without supervision, in benchmark texts. Once validated, TFMNs were applied to the case study of the gender gap in science, which was strongly linked to distorted mindsets by recent studies. Focusing over social media perception and online discourse, this work analysed 10,000 relevant tweets. "Gender" and "gap" elicited a mostly positive perception, with a trustful/joyous emotional profile and semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of "woman" highlighted discussion about sexual harassment and stereotype threat (a form of implicit cognitive bias) relative to women in science "sacrificing personal skills for success". The reconstructed perception of "man" highlighted social users' awareness of the myth of male superiority in science. No anger was detected around "person", suggesting that gap-focused discourse got less tense around genderless terms. No stereotypical perception of "scientist" was identified online, differently from real-world surveys. The overall analysis identified the online discourse as promoting a mostly stereotype-free, positive/trustful perception of gender disparity, aware of implicit/explicit biases and projected to closing the gap. TFMNs opened new ways for investigating perceptions in different groups, offering detailed data-informed grounding for policy making.

Includes members:
  Member "from topic networks to distributed cognitive maps zipfian topic universes in the area of volunteered geographic information":
    also connected through other hyperedges:
      (none)
  Member "investigating italian disinformation spreading on twitter in the context of 2019 european elections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a multi layer approach to disinformation detection on twitter")
  Member "historical comparison of gender inequality in scientific careers across countries and disciplines":
    also connected through other hyperedges:
      (none)
  Member "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "text authorship identified using the dynamics of word co occurrence networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "a complex network approach to stylometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "probing the topological properties of complex networks modeling short written texts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")

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)