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: "failedrevolutions using twitter to study the antecedents of isis support"
  Abstract: Within a fairly short amount of time, the Islamic State of Iraq and Syria (ISIS) has managed to put large swaths of land in Syria and Iraq under their control. To many observers, the sheer speed at which this "state" was established was dumbfounding. To better understand the roots of this organization and its supporters we present a study using data from Twitter. We start by collecting large amounts of Arabic tweets referring to ISIS and classify them into pro-ISIS and anti-ISIS. This classification turns out to be easily done simply using the name variants used to refer to the organization: the full name and the description as "state" is associated with support, whereas abbreviations usually indicate opposition. We then "go back in time" by analyzing the historic timelines of both users supporting and opposing and look at their pre-ISIS period to gain insights into the antecedents of support. To achieve this, we build a classifier using pre-ISIS data to "predict", in retrospect, who will support or oppose the group. The key story that emerges is one of frustration with failed Arab Spring revolutions. ISIS supporters largely differ from ISIS opposition in that they refer a lot more to Arab Spring uprisings that failed. We also find temporal patterns in the support and opposition which seems to be linked to major news, such as reported territorial gains, reports on gruesome acts of violence, and reports on airstrikes and foreign intervention.

Connected through hyperedge HE-1 (induced by source paper "quantifying polarization on twitter the kavanaugh nomination"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "devam vs tamam 2018 turkish elections"
    - "automatic stance detection using end to end memory networks"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "bag of tricks for efficient text classification"
    - "content and network dynamics behind egyptian political polarization on twitter"

Connected through hyperedge HE-2 (induced by source paper "social influence and radicalization a social data analytics study"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "data curation apis"
    - "least cost influence maximization across multiple social networks"
    - "klout score measuring influence across multiple social networks"
    - "online social media in the syria conflict encompassing the extremes and the in betweens"
    - "big data and cross document coreference resolution current state and future opportunities"
    - "maximizing social influence in nearly optimal time"
    - "on budgeted influence maximization in social networks"

Connected through hyperedge HE-3 (induced by source paper "embedding based qualitative analysis of polarization in turkey"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "devam vs tamam 2018 turkish elections"
    - "community interaction and conflict on the web"
    - "to kavanaugh or not to kavanaugh that is the polarizing question"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "semantics derived automatically from language corpora contain human like biases"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-4 (induced by source paper "predicting the topical stance of media and popular twitter users"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "to kavanaugh or not to kavanaugh that is the polarizing question"
    - "bag of tricks for efficient text classification"
    - "content and network dynamics behind egyptian political polarization on twitter"

Connected through hyperedge HE-5 (induced by source paper "unsupervised user stance detection on twitter"):
  with members:
    - "devam vs tamam 2018 turkish elections"
    - "content and network dynamics behind egyptian political polarization on twitter"

Connected through hyperedge HE-6 (induced by source paper "arabs and atheism religious discussions in the arab twittersphere"):
  with members:
    - "u s religious landscape on twitter"

Connected through hyperedge HE-7 (induced by source paper "deception strategies and threats for online discussions"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "anatomy of an online misinformation network"
    - "the spread of low credibility content by social bots"
    - "soc2seq social embedding meets conversation model"
    - "reuters tracer toward automated news production using large scale social media data"
    - "early detection of promoted campaigns on social media"
    - "discovery retrieval and analysis of star wars botnet in twitter"
    - "limited individual attention and online virality of low quality information"

Connected through hyperedge HE-8 (induced by source paper "the rise of jihadist propaganda on social networks"):
  with members:
    - "contagion dynamics of extremist propaganda in social networks"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "evolution of online user behavior during a social upheaval"
    - "the digital evolution of occupy wall street"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

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)