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 "a benchmark dataset for learning to intervene in online hate speech"):
  Abstract: Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech. As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets collected from Gab and Reddit. These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk Workers. In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.

Includes members:
  Member "hierarchical cvae for fine grained hate speech classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual and multi aspect hate speech analysis")
  Member "large scale crowdsourcing and characterization of twitter abusive behavior":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition")
  Member "peer to peer hate hate speech instigators and their targets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conan counter narratives through nichesourcing a multilingual dataset of responses to fight online hate speech")
  Member "hate lingo a target based linguistic analysis of hate speech in social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a framework for the computational linguistic analysis of dehumanization")
  Member "leveraging intra user and inter user representation learning for automated hate speech detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal effects of unmoderated hate speech in gab")
  Member "mean birds detecting aggression and bullying on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "angry birds flock together aggression propagation on social media")
  Member "automated hate speech detection and the problem of offensive language":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "deep learning for hate speech detection in tweets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook")

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)