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 to ask for a favor a case study on the success of altruistic requests"
  Abstract: Requests are at the core of many social media systems such as question & answer sites and online philanthropy communities. While the success of such requests is critical to the success of the community, the factors that lead community members to satisfy a request are largely unknown. Success of a request depends on factors like who is asking, how they are asking, when are they asking, and most critically what is being requested, ranging from small favors to substantial monetary donations. We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors. Drawing from social psychology literature, we extract high-level social features from text that operationalize social relations between recipient and donor and demonstrate that these extracted relations are predictive of success. More specifically, we find that clearly communicating need through the narrative is essential and that that linguistic indications of gratitude, evidentiality, and generalized reciprocity, as well as high status of the asker further increase the likelihood of success. Building on this understanding, we develop a model that can predict the success of unseen requests, significantly improving over several baselines. We link these findings to research in psychology on helping behavior, providing a basis for further analysis of success in social media systems.

Connected through hyperedge HE-1 (induced by source paper "conversations gone awry detecting early signs of conversational failure"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "asking too much the rhetorical role of questions in political discourse"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "interpreting neural networks to improve politeness comprehension"
    - "a piece of my mind a sentiment analysis approach for online dispute detection"
    - "ex machina personal attacks seen at scale"
    - "conversational markers of constructive discussions"

Connected through hyperedge HE-2 (induced by source paper "how to ask for technical help evidence based guidelines for writing questions on stack overflow"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "bootstrapping a lexicon for emotional arousal in software engineering"
    - "emotxt a toolkit for emotion recognition from text"
    - "recommending insightful comments for source code using crowdsourced knowledge"
    - "do feelings matter on the correlation of affects and the self assessed productivity in software engineering"
    - "the role of emotions in contributors activity a case study on the gentoo community"

Connected through hyperedge HE-3 (induced by source paper "mobilizing the trump train understanding collective action in a political trolling community"):
  with members:
    - "an army of me sockpuppets in online discussion communities"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "botivist calling volunteers to action using online bots"
    - "antisocial behavior in online discussion communities"
    - "participatory militias an analysis of an armed movement s online audience"
    - "evolution of reddit from the front page of the internet to a self referential community"

Connected through hyperedge HE-4 (induced by source paper "using sentiment induction to understand variation in gendered online communities"):
  with members:
    - "community identity and user engagement in a multi community landscape"
    - "loyalty in online communities"
    - "ultradense word embeddings by orthogonal transformation"
    - "talking to the crowd what do people react to in online discussions"
    - "gender identity and lexical variation in social media"

Connected through hyperedge HE-5 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "beyond exchangeability the chinese voting process"
    - "learning distributed representations of sentences from unlabelled data"
    - "understanding image virality"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "identifying the social signals that drive online discussions a case study of reddit communities"):
  with members:
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "the impact of crowds on news engagement a reddit case study"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "characterizing the language of online communities and its relation to community reception"
    - "trend of narratives in the age of misinformation"
    - "breaking the news first impressions matter on online news"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "talking to the crowd what do people react to in online discussions"

Connected through hyperedge HE-7 (induced by source paper "asking too much the rhetorical role of questions in political discourse"):
  with members:
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "interpreting neural networks to improve politeness comprehension"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "quotus the structure of political media coverage as revealed by quoting patterns"
    - "a computational approach to politeness with application to social factors"
    - "scikit learn machine learning in python"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-8 (induced by source paper "dialectic enhancing text input fields with automatic feedback to improve social content writing quality"):
  with members:
    - "adversarial generation of natural language"
    - "iris a conversational agent for complex tasks"
    - "rationalizing neural predictions"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "inferring networks of substitutable and complementary products"
    - "can cascades be predicted"

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.CL(Computation and Language)