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: "like sheep among wolves characterizing hateful users on twitter"
  Abstract: Hateful speech in Online Social Networks (OSNs) is a key challenge for companies and governments, as it impacts users and advertisers, and as several countries have strict legislation against the practice. This has motivated work on detecting and characterizing the phenomenon in tweets, social media posts and comments. However, these approaches face several shortcomings due to the noisiness of OSN data, the sparsity of the phenomenon, and the subjectivity of the definition of hate speech. This works presents a user-centric view of hate speech, paving the way for better detection methods and understanding. We collect a Twitter dataset of $100,386$ users along with up to $200$ tweets from their timelines with a random-walk-based crawler on the retweet graph, and select a subsample of $4,972$ to be manually annotated as hateful or not through crowdsourcing. We examine the difference between user activity patterns, the content disseminated between hateful and normal users, and network centrality measurements in the sampled graph. Our results show that hateful users have more recent account creation dates, and more statuses, and followees per day. Additionally, they favorite more tweets, tweet in shorter intervals and are more central in the retweet network, contradicting the "lone wolf" stereotype often associated with such behavior. Hateful users are more negative, more profane, and use less words associated with topics such as hate, terrorism, violence and anger. We also identify similarities between hateful/normal users and their 1-neighborhood, suggesting strong homophily.

Connected through hyperedge HE-1 (induced by source paper "discovering differential features adversarial learning for information credibility evaluation"):
  with members:
    - "the role of user profile for fake news detection"
    - "different absorption from the same sharing sifted multi task learning for fake news detection"
    - "multinomial adversarial networks for multi domain text classification"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake cures user centric modeling of health misinformation in social media"
    - "deep neural networks for bot detection"
    - "multi task learning by deep collaboration and application in facial landmark detection"
    - "neural networks for information retrieval"

Connected through hyperedge HE-2 (induced by source paper "variational spectral graph convolutional networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "bayesian semi supervised learning with graph gaussian processes"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "empath understanding topic signals in large scale text"

Connected through hyperedge HE-3 (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-4 (induced by source paper "analyzing the hate and counter speech accounts on twitter"):
  with members:
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "thou shalt not hate countering online hate speech"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "characterizing and detecting hateful users on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "deep learning for hate speech detection in tweets"

Connected through hyperedge HE-5 (induced by source paper "analyzing right wing youtube channels hate violence and discrimination"):
  with members:
    - "trollspot detecting misbehavior in commenting platforms"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "semantics derived automatically from language corpora contain human like biases"
    - "empath understanding topic signals in large scale text"
    - "shirtless and dangerous quantifying linguistic signals of gender bias in an online fiction writing community"
    - "participatory militias an analysis of an armed movement s online audience"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "hate speech detection a solved problem the challenging case of long tail on twitter"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "data sets word embeddings learned from tweets and general data"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "large scale crowdsourcing and characterization of twitter abusive behavior"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "making the most of tweet inherent features for social spam detection on twitter"
    - "the social world of content abusers in community question answering"

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)