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: "how did the suicide act and speak differently online behavioral and linguistic features of china s suicide microblog users"
  Abstract: BACKGROUND: Suicide issue is of great concern in China. Social media provides an active approach to understanding suicide individuals in terms of their behavior and language use. Aims: This study investigates how suicide Microblog users in China act and speak differently on social media from others. METHODS: Hypothesis testing in behavioral and linguistic features was performed between a target group of 33 Chinese Microblog users who have committed suicide and a control group of 30 active users without suicidal ideation. RESULTS: Suicide group significantly outnumbered control group in the extent of openly published posts and self-reference, and the intensity of using 7 word categories: negative words/social process words/cognitive process words/emotion process words/negative emotion words/exclusive words/physiological process words. Limitations: Information collection and confirmation of suicide users remain difficult. CONCLUSIONS: It is revealed that suicide people vary from others in certain behavioral and linguistic features in social media. This study fills the niche of suicide studies by noting specified indicators of suicide ideation for Chinese individuals online, providing insights of constructing an online alarm system for early detection and intervention of suicidal individuals. Language: en

Connected through hyperedge HE-1 (induced by source paper "latent suicide risk detection on microblog via suicide oriented word embeddings and layered attention"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fasttext zip compressing text classification models"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "detecting suicidal ideation in chinese microblogs with psychological lexicons"
    - "using linguistic features to estimate suicide probability of chinese microblog users"
    - "suicide ideation of individuals in online social networks"

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)