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: "mean birds detecting aggression and bullying on twitter"
  Abstract: In recent years, bullying and aggression against users on social media have grown significantly, causing serious consequences to victims of all demographics. In particular, cyberbullying affects more than half of young social media users worldwide, and has also led to teenage suicides, prompted by prolonged and/or coordinated digital harassment. Nonetheless, tools and technologies for understanding and mitigating it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of cyberbullies and aggressors, and what features distinguish them from regular users. We find that bully users post less, participate in fewer online communities, and are less popular than normal users, while aggressors are quite popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, achieving over 90% AUC.

Connected through hyperedge HE-1 (induced by source paper "angry birds flock together aggression propagation on social media"):
  with members:
    - "detecting cyberbullying and cyberaggression in social media"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "automated hate speech detection and the problem of offensive language"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "the social world of content abusers in community question answering"
    - "opinion maximization in social networks"

Connected through hyperedge HE-2 (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"
    - "peer to peer hate hate speech instigators and their targets"
    - "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"
    - "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-3 (induced by source paper "multilingual and multi aspect hate speech analysis"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "examining a hate speech corpus for hate speech detection and popularity prediction"
    - "hierarchical cvae for fine grained hate speech classification"
    - "reducing gender bias in abusive language detection"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-4 (induced by source paper "detecting cyberbullying and cyberaggression in social media"):
  with members:
    - "measuring gamergate a tale of hate sexism and bullying"
    - "poised spotting twitter spam off the beaten paths"
    - "hate is not binary studying abusive behavior of gamergate on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "a longitudinal measurement study of 4chan s politically incorrect forum and its effect on the web"
    - "deep residual learning for image recognition"
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-5 (induced by source paper "transfer learning for hate speech detection in social media"):
  with members:
    - "a unified deep learning architecture for abuse detection"
    - "deep contextualized word representations"
    - "automated hate speech detection and the problem of offensive language"
    - "attention is all you need"
    - "one step and two step classification for abusive language detection on twitter"
    - "ex machina personal attacks seen at scale"
    - "antisocial behavior in online discussion communities"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "do you really follow them automatic detection of credulous twitter users"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "arming the public with artificial intelligence to counter social bots"
    - "the spread of low credibility content by social bots"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "the rise of social bots"
    - "fame for sale"
    - "estimating continuous distributions in bayesian classifiers"

Connected through hyperedge HE-7 (induced by source paper "bridging information security and environmental criminology research to better mitigate cybercrime"):
  with members:
    - "a unified deep learning architecture for abuse detection"
    - "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"
    - "what s in a name understanding profile name reuse on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

Connected through hyperedge HE-8 (induced by source paper "tweeting mps digital engagement between citizens and members of parliament in the uk"):
  with members:
    - "a long term analysis of polarization on twitter"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "content and network dynamics behind egyptian political polarization on twitter"

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)