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 "hatemonitors language agnostic abuse detection in social media"):
  Abstract: Reducing hateful and offensive content in online social media pose a dual problem for the moderators. On the one hand, rigid censorship on social media cannot be imposed. On the other, the free flow of such content cannot be allowed. Hence, we require efficient abusive language detection system to detect such harmful content in social media. In this paper, we present our machine learning model, HateMonitor, developed for Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC), a shared task at FIRE 2019. We have used a Gradient Boosting model, along with BERT and LASER embeddings, to make the system language agnostic. Our model came at First position for the German sub-task A. We have also made our model public at this https URL .

Includes members:
  Member "the enemy among us detecting hate speech with threats based othering language embeddings":
    also connected through other hyperedges:
      (none)
  Member "a quantitative approach to understanding online antisemitism":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic search of memes on twitter")
  Member "cyber hate classification othering language and paragraph embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "impact of sentiment detection to recognize toxic and subversive online comments")
  Member "hierarchical cvae for fine grained hate speech classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmark dataset for learning to intervene in online hate speech")
  Member "hateminers detecting hate speech against women":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spread of hate speech in online social media")
  Member "spread of hate speech in online social media":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal effects of unmoderated hate speech in gab")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "detecting weak and strong islamophobic hate speech on social media":
    also connected through other hyperedges:
      (none)

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.SI(Social and Information Networks)