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: "automatic detection of cyberbullying in social media text"
  Abstract: While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a cyberbullying corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for the task. Experiments on a hold-out test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1 score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems.

Connected through hyperedge HE-1 (induced by source paper "current limitations in cyberbullying detection on evaluation criteria reproducibility and data scarcity"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning for detecting cyberbullying across multiple social media platforms"
    - "enriching word vectors with subword information"
    - "evaluating unsupervised dutch word embeddings as a linguistic resource"
    - "prediction of cyberbullying incidents on the instagram social network"
    - "a c lstm neural network for text classification"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "facilitating on line opinion dynamics by mining expressions of causation the case of climate change debates on the guardian"):
  with members:
    - "automatic detection of online jihadist hate speech"

Connected through hyperedge HE-3 (induced by source paper "safecity understanding diverse forms of sexual harassment personal stories"):
  with members:
    - "deep learning for detecting cyberbullying across multiple social media platforms"
    - "depression severity estimation from multiple modalities"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"
    - "interpreting neural networks to improve politeness comprehension"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing and understanding neural models in nlp"
    - "a c lstm neural network for text classification"
    - "adam a method for stochastic optimization"

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.CL(Computation and Language)