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: "deep learning for detecting cyberbullying across multiple social media platforms"
  Abstract: Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based models can overcome all three bottlenecks. Knowledge learned by these models on one dataset can be transferred to other datasets. We performed extensive experiments using three real-world datasets: Formspring (12k posts), Twitter (16k posts), and Wikipedia(100k posts). Our experiments provide several useful insights about cyberbullying detection. To the best of our knowledge, this is the first work that systematically analyzes cyberbullying detection on various topics across multiple SMPs using deep learning based models and transfer learning.

Connected through hyperedge HE-1 (induced by source paper "multi label categorization of accounts of sexism using a neural framework"):
  with members:
    - "safecity understanding diverse forms of sexual harassment personal stories"
    - "deep contextualized word representations"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "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 "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"
    - "automatic detection of cyberbullying in social media text"
    - "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-3 (induced by source paper "context sensitive malicious spelling error correction"):
  with members:
    - "impact of content features for automatic online abuse detection"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "neural networks for text correction and completion in keyboard decoding"
    - "geometry of compositionality"
    - "polyglot distributed word representations for multilingual nlp"
    - "distributed representations of words and phrases and their compositionality"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-4 (induced by source paper "uncover sexual harassment patterns from personal stories by joint key element extraction and categorization"):
  with members:
    - "safecity understanding diverse forms of sexual harassment personal stories"
    - "enriching word vectors with subword information"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "safecity understanding diverse forms of sexual harassment personal stories"):
  with members:
    - "automatic detection of cyberbullying in social media text"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "cyberbullying detection in social networks using deep learning based models a reproducibility study"):
  with members:
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "ex machina personal attacks seen at scale"
    - "very deep convolutional networks for large scale image recognition"

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.IR(Information Retrieval)