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: "peer to peer hate hate speech instigators and their targets"
  Abstract: While social media has become an empowering agent to individual voices and freedom of expression, it also facilitates anti-social behaviors including online harassment, cyberbullying, and hate speech. In this paper, we present the first comparative study of hate speech instigators and target users on Twitter. Through a multi-step classification process, we curate a comprehensive hate speech dataset capturing various types of hate. We study the distinctive characteristics of hate instigators and targets in terms of their profile self-presentation, activities, and online visibility. We find that hate instigators target more popular and high profile Twitter users, and that participating in hate speech can result in greater online visibility. We conduct a personality analysis of hate instigators and targets and show that both groups have eccentric personality facets that differ from the general Twitter population. Our results advance the state of the art of understanding online hate speech engagement.

Connected through hyperedge HE-1 (induced by source paper "a benchmark dataset for learning to intervene in online hate speech"):
  with members:
    - "hierarchical cvae for fine grained hate speech classification"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "leveraging intra user and inter user representation learning for automated hate speech detection"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "recognizing explicit and implicit hate speech using a weakly supervised two path bootstrapping approach"

Connected through hyperedge HE-2 (induced by source paper "conan counter narratives through nichesourcing a multilingual dataset of responses to fight online hate speech"):
  with members:
    - "bleaching text abstract features for cross lingual gender prediction"
    - "training millions of personalized dialogue agents"
    - "examining a hate speech corpus for hate speech detection and popularity prediction"
    - "hate speech dataset from a white supremacy forum"
    - "thou shalt not hate countering online hate speech"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "analyzing the hate and counter speech accounts on twitter"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-3 (induced by source paper "exploring hate speech detection in multimodal publications"):
  with members:
    - "learning to learn from web data through deep semantic embeddings"
    - "recurrent fusion network for image captioning"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "one step and two step classification for abusive language detection on twitter"
    - "improving text proposals for scene images with fully convolutional networks"
    - "a web of hate tackling hateful speech in online social spaces"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

Connected through hyperedge HE-4 (induced by source paper "race ethnicity and national origin based discrimination in social media and hate crimes across 100 u s cities"):
  with members:
    - "the effect of extremist violence on hateful speech online"
    - "socio spatial self organizing maps using social media to assess relevant geographies for exposure to social processes"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "characterizing and detecting hateful users on twitter"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "ex machina personal attacks seen at scale"
    - "empath understanding topic signals in large scale text"

Connected through hyperedge HE-5 (induced by source paper "spread of hate speech in online social media"):
  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"
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "hierarchical cvae for fine grained hate speech classification"
    - "thou shalt not hate countering online hate speech"
    - "hateminers detecting hate speech against women"
    - "analyzing right wing youtube channels hate violence and discrimination"

Connected through hyperedge HE-6 (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"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "on the origins of memes by means of fringe web communities"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-7 (induced by source paper "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"):
  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"
    - "community interaction and conflict on the web"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "opinion conflicts an effective route to detect incivility in twitter"
    - "mean birds detecting aggression and bullying on twitter"
    - "poised spotting twitter spam off the beaten paths"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

Connected through hyperedge HE-8 (induced by source paper "opinion conflicts an effective route to detect incivility in twitter"):
  with members:
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "mean birds detecting aggression and bullying on twitter"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "hate is not binary studying abusive behavior of gamergate on twitter"
    - "analyzing the targets of hate in online social media"
    - "character level convolutional networks for text classification"

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)