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 "causal effects of brevity on style and success in social media"):
  Abstract: In online communities, where billions of people strive to propagate their messages, understanding how wording affects success is of primary importance. In this work, we are interested in one particularly salient aspect of wording: brevity. What is the causal effect of brevity on message success? What are the linguistic traits of brevity? When is brevity beneficial, and when is it not? Whereas most prior work has studied the effect of wording on style and success in observational setups, we conduct a controlled experiment, in which crowd workers shorten social media posts to prescribed target lengths and other crowd workers subsequently rate the original and shortened versions. This allows us to isolate the causal effect of brevity on the success of a message. We find that concise messages are on average more successful than the original messages up to a length reduction of 30-40%. The optimal reduction is on average between 10% and 20%. The observed effect is robust across different subpopulations of raters and is the strongest for raters who visit social media on a daily basis. Finally, we discover unique linguistic and content traits of brevity and correlate them with the measured probability of success in order to distinguish effective from ineffective shortening strategies. Overall, our findings are important for developing a better understanding of the effect of brevity on the success of messages in online social media.

Includes members:
  Member "how constraints affect content the case of twitter s switch from 140 to 280 characters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a total error framework for digital traces of humans")
  Member "anyone can become a troll causes of trolling behavior in online discussions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "neural paraphrase generation with stacked residual lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "paraphrase generation with latent bag of words")
  Member "tgsum build tweet guided multi document summarization dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gamewikisum a novel large multi document summarization dataset")
  Member "the effect of wording on message propagation topic and author controlled natural experiments on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans")
  Member "a computational approach to politeness with application to social factors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "exploring text virality in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity")
  Member "for the sake of simplicity unsupervised extraction of lexical simplifications from wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automatic fact guided sentence modification")

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)