human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cyber hate classification othering language and paragraph embedding"):
  Abstract: Hateful and offensive language (also known as hate speech or cyber hate) posted and widely circulated via the World Wide Web can be considered as a key risk factor for individual and societal tension linked to regional instability. Automated Web-based hate speech detection is important for the observation and understanding trends of societal tension. In this research, we improve on existing research by proposing different data mining feature extraction methods. While previous work has involved using lexicons, bags-of-words or probabilistic parsing approach (e.g. using Typed Dependencies), they all suffer from a similar issue which is that hate speech can often be subtle and indirect, and depending on individual words or phrases can lead to a significant number of false negatives. This problem motivated us to conduct new experiments to identify subtle language use, such as references to immigration or job prosperity in a hateful context. We propose a novel 'Othering Lexicon' to identify these subtleties and we incorporate our lexicon with embedding learning for feature extraction and subsequent classification using a neural network approach. Our method first explores the context around othering terms in a corpus, and identifies context patterns that are relevant to the othering context. These patterns are used along with the othering pronoun and hate speech terms to build our 'Othering Lexicon'. Embedding algorithm has the superior characteristic that the similar words have a closer distance, which is helpful to train our classifier on the negative and positive classes. For validation, several experiments were conducted on different types of hate speech, namely religion, disability, race and sexual orientation, with F-measure scores for classifying hateful instances obtained through applying our model of 0.93, 0.95, 0.97 and 0.92 respective.

Includes members:
  Member "automated hate speech detection and the problem of offensive language":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "detecting hate speech in social media":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "offensive language identification in greek")
  Member "deep learning for hate speech detection in tweets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook")
  Member "a web of hate tackling hateful speech in online social spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "temporal effects of unmoderated hate speech in gab")
  Member "recognizing explicit and implicit hate speech using a weakly supervised two path bootstrapping approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a just and comprehensive strategy for using nlp to address online abuse")
  Member "ex machina personal attacks seen at scale":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition")
  Member "analyzing the targets of hate in online social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a framework for the computational linguistic analysis of dehumanization")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

Question: Please tell me which class the source paper 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)