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: "degree based classification of harmful speech using twitter data"
  Abstract: Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst it, the classification needs to be based on complex ramifications which needs to be defined and hold accountable for, other than racist, sexist or against some particular group and community. This paper primarily describes how we created an ontological classification of harmful speech based on degree of hateful intent, and used it to annotate twitter data accordingly. The key contribution of this paper is the new dataset of tweets we created based on ontological classes and degrees of harmful speech found in the text. We also propose supervised classification system for recognizing these respective harmful speech classes in the texts hence.

Connected through hyperedge HE-1 (induced by source paper "towards countering hate speech and personal attack in social media"):
  with members:
    - "a unified deep learning architecture for abuse detection"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "universal language model fine tuning for text classification"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"

Connected through hyperedge HE-2 (induced by source paper "hate speech dataset from a white supremacy forum"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "lstm a search space odyssey"
    - "a web of hate tackling hateful speech in online social spaces"
    - "measuring the reliability of hate speech annotations the case of the european refugee crisis"
    - "analyzing the targets of hate in online social media"

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)