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: "a quantitative approach to understanding online antisemitism"
  Abstract: A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand this worrying trend. In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million comments and images from alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and the Twitter clone, Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the "Unite the Right" rally in Charlottesville. Furthermore, this antisemitism appears in tandem with sharp increases in white ethnic nationalist content on the same communities. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic "Happy Merchant" meme, and in particular how these fringe communities influence its propagation to more mainstream services like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our open and scientifically grounded methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of antisemitism online.

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"
    - "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"
    - "clustering memes in social media streams"

Connected through hyperedge HE-2 (induced by source paper "understanding the incel community on youtube"):
  with members:
    - "auditing radicalization pathways on youtube"
    - "disturbed youtube for kids characterizing and detecting inappropriate videos targeting young children"
    - "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities"):
  with members:
    - "characterizing the use of images by state sponsored troll accounts on twitter"
    - "characterizing the use of images in state sponsored information warfare operations by russian trolls on twitter"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "false information on web and social media a survey"
    - "on the origins of memes by means of fringe web communities"

Connected through hyperedge HE-4 (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership"):
  with members:
    - "social network analysis for social neuroscientists"
    - "its all in a name detecting and labeling bots by their name"
    - "centrality in modular networks"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "on the origins of memes by means of fringe web communities"
    - "a long term analysis of polarization on twitter"
    - "botornot a system to evaluate social bots"
    - "think locally act locally detection of small medium sized and large communities in large networks"

Connected through hyperedge HE-5 (induced by source paper "and we will fight for our race a measurement study of genetic testing conversations on reddit and 4chan"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "the effect of extremist violence on hateful speech online"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "23andme confirms i m super white analyzing twitter discourse on genetic testing"
    - "on the origins of memes by means of fringe web communities"
    - "characterizing and detecting hateful users on twitter"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-6 (induced by source paper "hatemonitors language agnostic abuse detection in social media"):
  with members:
    - "the enemy among us detecting hate speech with threats based othering language embeddings"
    - "cyber hate classification othering language and paragraph embedding"
    - "hierarchical cvae for fine grained hate speech classification"
    - "hateminers detecting hate speech against women"
    - "spread of hate speech in online social media"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "detecting weak and strong islamophobic hate speech on social media"
    - "understanding abuse a typology of abusive language detection subtasks"

Connected through hyperedge HE-7 (induced by source paper "characterizing the use of images in state sponsored information warfare operations by russian trolls on twitter"):
  with members:
    - "tracking the digital traces of russian trolls distinguishing the roles and strategy of trolls on twitter"
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "analysis of strategy and spread of russia sponsored content in the us in 2017"
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "mobilizing the trump train understanding collective action in a political trolling community"

Connected through hyperedge HE-8 (induced by source paper "examining untempered social media analyzing cascades of polarized conversations"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "spread of hate speech in online social media"
    - "on the origins of memes by means of fringe web communities"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "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.CY(Computers and Society)