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: "detecting the hate code on social media"
  Abstract: Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To prevent violating the abuse policies of social media platforms and also to avoid detection by automatic systems like Google's Conversation AI, racists have begun to use a code (a movement termed Operation Google). This involves substituting references to communities by benign words that seem out of context, in hate filled posts or Tweets. For example, users have used the words Googles and Bings to represent the African-American and Asian communities, respectively. By generating the list of users who post such content, we move a step forward from classifying tweets by allowing us to study the usage pattern of these concentrated set of users.

Connected through hyperedge HE-1 (induced by source paper "metoo on campus studying college sexual assault at scale using data reported on social media"):
  with members:
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "multimodal social media analysis for gang violence prevention"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "using social media to promote stem education matching college students with role models"

Connected through hyperedge HE-2 (induced by source paper "learning invariant representations of social media users"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "deep learning is robust to massive label noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"

Connected through hyperedge HE-3 (induced by source paper "a quantitative approach to understanding online antisemitism"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a unified deep learning architecture for abuse detection"
    - "automatic detection of online jihadist hate speech"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "understanding web archiving services and their mis use on social media"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "on the origins of memes by means of fringe web communities"

Connected through hyperedge HE-4 (induced by source paper "characterizing and detecting hateful users on twitter"):
  with members:
    - "the wisdom of polarized crowds"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "inductive representation learning on large graphs"
    - "deep learning for hate speech detection in tweets"
    - "representation learning on graphs methods and applications"
    - "hate is not binary studying abusive behavior of gamergate on twitter"

Connected through hyperedge HE-5 (induced by source paper "like sheep among wolves characterizing hateful users on twitter"):
  with members:
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "inductive representation learning on large graphs"
    - "antagonism also flows through retweets the impact of out of context quotes in opinion polarization analysis"
    - "tweet2vec character based distributed representations for social media"
    - "measuring the reliability of hate speech annotations the case of the european refugee crisis"
    - "empath understanding topic signals in large scale text"
    - "analyzing the targets of hate in online social media"

Connected through hyperedge HE-6 (induced by source paper "understanding abuse a typology of abusive language detection subtasks"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "ex machina personal attacks seen at scale"
    - "measuring the reliability of hate speech annotations the case of the european refugee crisis"
    - "a longitudinal measurement study of 4chan s politically incorrect forum and its effect on the web"

Connected through hyperedge HE-7 (induced by source paper "surfacing contextual hate speech words within social media"):
  with members:
    - "enriching word vectors with subword information"
    - "distributed representations of words and phrases and their compositionality"

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)