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: "analyzing right wing youtube channels hate violence and discrimination"
  Abstract: As of 2018, YouTube, the major online video sharing website, hosts multiple channels promoting right-wing content. In this paper, we observe issues related to hate, violence and discriminatory bias in a dataset containing more than 7,000 videos and 17 million comments. We investigate similarities and differences between users' comments and video content in a selection of right-wing channels and compare it to a baseline set using a three-layered approach, in which we analyze (a) lexicon, (b) topics and (c) implicit biases present in the texts. Among other results, our analyses show that right-wing channels tend to (a) contain a higher degree of words from "negative'' semantic fields, (b) raise more topics related to war and terrorism, and (c) demonstrate more discriminatory bias against Muslims (in videos) and towards LGBT people (in comments). Our findings shed light not only into the collective conduct of the YouTube community promoting and consuming right-wing content, but also into the general behavior of YouTube users.

Connected through hyperedge HE-1 (induced by source paper "from pick up artists to incels a data driven sketch of the manosphere"):
  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"
    - "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"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"

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"
    - "a quantitative approach to understanding online antisemitism"
    - "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (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"
    - "a quantitative approach to understanding online antisemitism"
    - "the effect of extremist violence on hateful speech online"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "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-4 (induced by source paper "a bert based transfer learning approach for hate speech detection in online social media"):
  with members:
    - "and we will fight for our race a measurement study of genetic testing conversations on reddit and 4chan"
    - "racial bias in hate speech and abusive language detection datasets"
    - "a unified deep learning architecture for abuse detection"
    - "challenges in discriminating profanity from hate speech"
    - "the effect of extremist violence on hateful speech online"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (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"
    - "a quantitative approach to understanding online antisemitism"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "disturbed youtube for kids characterizing and detecting inappropriate videos targeting young children"):
  with members:
    - "combating the elsagate phenomenon deep learning architectures for disturbing cartoons"
    - "characterizing videos audience and advertising in youtube channels for kids"
    - "kidstube detection characterization and analysis of child unsafe content promoters on youtube"
    - "tensorflow a system for large scale machine learning"
    - "going deeper with convolutions"
    - "network analysis of recurring youtube spam campaigns"
    - "mining user comment activity for detecting forum spammers in youtube"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (induced by source paper "algorithmic extremism examining youtube s rabbit hole of radicalization"):
  with members:
    - "auditing radicalization pathways on youtube"
    - "spider and the flies focused crawling on tumblr to detect hate promoting communities"
    - "sentiment analysis on youtube a brief survey"
    - "force directed graph drawing using social gravity and scaling"

Connected through hyperedge HE-8 (induced by source paper "auditing radicalization pathways on youtube"):
  with members:
    - "disturbed youtube for kids characterizing and detecting inappropriate videos targeting young children"
    - "on the origins of memes by means of fringe web communities"

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)