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 constraints affect content the case of twitter s switch from 140 to 280 characters"
  Abstract: It is often said that constraints affect creative production, both in terms of form and quality. Online social media platforms frequently impose constraints on the content that users can produce, limiting the range of possible contributions. Do these restrictions tend to push creators towards producing more or less successful content? How do creators adapt their contributions to fit the limits imposed by social media platforms? To answer these questions, we conduct an observational study of a recent event: on November 7, 2017, Twitter changed the maximum allowable length of a tweet from 140 to 280 characters, thereby significantly altering its signature constraint. In the first study of this switch, we compare tweets with nearly or exactly 140 characters before the change to tweets of the same length posted after the change. This setup enables us to characterize how users alter their tweets to fit the constraint and how this affects their tweets' success. We find that in response to a length constraint, users write more tersely, use more abbreviations and contracted forms, and use fewer definite articles. Also, although in general tweet success increases with length, we find initial evidence that tweets made to fit the 140-character constraint tend to be more successful than similar-length tweets written when the constraint was removed, suggesting that the length constraint improved tweet quality.

Connected through hyperedge HE-1 (induced by source paper "a total error framework for digital traces of humans"):
  with members:
    - "model cards for model reporting"
    - "demographic inference and representative population estimates from multilingual social media data"
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "datasheets for datasets"
    - "false information on web and social media a survey"
    - "systematically monitoring social media the case of the german federal election 2017"
    - "sampling from social networks with attributes"
    - "using social media to predict the future a systematic literature review"

Connected through hyperedge HE-2 (induced by source paper "message distortion in information cascades"):
  with members:
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "fake news detection in social networks via crowd signals"
    - "online human bot interactions detection estimation and characterization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "information evolution in social networks"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-3 (induced by source paper "causal effects of brevity on style and success in social media"):
  with members:
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "tgsum build tweet guided multi document summarization dataset"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "a computational approach to politeness with application to social factors"
    - "exploring text virality in social networks"
    - "for the sake of simplicity unsupervised extraction of lexical simplifications from wikipedia"

Connected through hyperedge HE-4 (induced by source paper "data driven analysis of tiny touchscreen performance with microjam"):
  with members:
    - "robojam a musical mixture density network for collaborative touchscreen interaction"
    - "6 seconds of sound and vision creativity in micro videos"

Connected through hyperedge HE-5 (induced by source paper "the rise and fall of the note changing paper lengths in acm cscw 2000 2018"):
  with members:
    - "structure and dynamics of coauthorship citation and impact within cscw"

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)