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 streams"
  Abstract: The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here, we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to “forget” old memes and replace them over time with the new ones. The evaluation of our framework is carried out using a dataset of Twitter trending topics. Over a 1-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.

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 "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-3 (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"
    - "traveling trends social butterflies or frequent fliers"
    - "clustering memes in social media"
    - "trends in social media persistence and decay"

Connected through hyperedge HE-4 (induced by source paper "event identification in social networks"):
  with members:
    - "fast and accurate annotation of short texts with wikipedia pages"

Connected through hyperedge HE-5 (induced by source paper "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator"):
  with members:
    - "the rise of social bots"
    - "on facebook most ties are weak"
    - "evolution of online user behavior during a social upheaval"
    - "predicting successful memes using network and community structure"
    - "the digital evolution of occupy wall street"
    - "traveling trends social butterflies or frequent fliers"
    - "the geospatial characteristics of a social movement communication network"
    - "clustering memes in social media"

Connected through hyperedge HE-6 (induced by source paper "parallel clustering of high dimensional social media data streams"):
  with members:
    - "the rise of social bots"
    - "traveling trends social butterflies or frequent fliers"
    - "clustering memes in social media"

Connected through hyperedge HE-7 (induced by source paper "who creates trends in online social media the crowd or opinion leaders"):
  with members:
    - "online popularity and topical interests through the lens of instagram"
    - "evolution of online user behavior during a social upheaval"
    - "the digital evolution of occupy wall street"
    - "traveling trends social butterflies or frequent fliers"
    - "popularity prediction in microblogging network a case study on sina weibo"
    - "cumulative effect in information diffusion empirical study on a microblogging network"
    - "virality prediction and community structure in social networks"
    - "dynamical classes of collective attention in twitter"

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)