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: "clustering memes in social media"
  Abstract: The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.

Connected through hyperedge HE-1 (induced by source paper "semantic search of memes on twitter"):
  with members:
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "a quantitative approach to understanding online antisemitism"
    - "dank learning generating memes using deep neural networks"
    - "on the origins of memes by means of fringe web communities"
    - "enriching word vectors with subword information"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-2 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "multiview deep learning for predicting twitter users location"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"

Connected through hyperedge HE-3 (induced by source paper "on the origins of memes by means of fringe web communities"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "memesequencer sparse matching for embedding image macros"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "on the efficiency of the information networks in social media"
    - "information evolution in social networks"
    - "tensorflow a system for large scale machine learning"
    - "predicting successful memes using network and community structure"
    - "truthy mapping the spread of astroturf in microblog streams"

Connected through hyperedge HE-4 (induced by source paper "early detection of promoted campaigns on social media"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "do cascades recur"
    - "the rise of social bots"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-5 (induced by source paper "style in the age of instagram predicting success within the fashion industry using social media"):
  with members:
    - "on facebook most ties are weak"
    - "online popularity and topical interests through the lens of instagram"
    - "clustering memes in social media streams"
    - "traveling trends social butterflies or frequent fliers"
    - "trends in social media persistence and decay"

Connected through hyperedge HE-6 (induced by source paper "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"):
  with members:
    - "automatic verbal aggression detection for russian and american imageboards"
    - "detecting state of aggression in sentences using cnn"
    - "antisocial behavior in online discussion communities"
    - "experimental analysis of popular smartphone apps offering anonymity ephemerality and end to end encryption"
    - "stfu noob predicting crowdsourced decisions on toxic behavior in online games"

Connected through hyperedge HE-7 (induced by source paper "a longitudinal measurement study of 4chan s politically incorrect forum and its effect on the web"):
  with members:
    - "automatic verbal aggression detection for russian and american imageboards"
    - "detecting state of aggression in sentences using cnn"
    - "antisocial behavior in online discussion communities"
    - "experimental analysis of popular smartphone apps offering anonymity ephemerality and end to end encryption"
    - "stfu noob predicting crowdsourced decisions on toxic behavior in online games"

Connected through hyperedge HE-8 (induced by source paper "quantifying the effect of sentiment on information diffusion in social media"):
  with members:
    - "the rise of social bots"
    - "measuring emotional contagion in social media"
    - "on predictability of rare events leveraging social media a machine learning perspective"
    - "on facebook most ties are weak"
    - "evolution of online user behavior during a social upheaval"
    - "can cascades be predicted"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "predicting successful memes using network and community structure"

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)