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 "who framed roger reindeer de censorship of facebook posts by snippet classification"):
  Abstract: This paper considers online news censorship and it concentrates on censorship of identities. Obfuscating identities may occur for disparate reasons, from military to judiciary ones. In the majority of cases, this happens to protect individuals from being identified and persecuted by hostile people. However, being the collaborative web characterised by a redundancy of information, it is not unusual that the same fact is reported by multiple sources, which may not apply the same restriction policies in terms of censorship. Also, the proven aptitude of social network users to disclose personal information leads to the phenomenon that comments to news can reveal the data withheld in the news itself. This gives us a mean to figure out who the subject of the censored news is. We propose an adaptation of a text analysis approach to unveil censored identities. The approach is tested on a synthesised scenario, which however resembles a real use case. Leveraging a text analysis based on a context classifier trained over snippets from posts and comments of Facebook pages, we achieve promising results. Despite the quite constrained settings in which we operate -- such as considering only snippets of very short length -- our system successfully detects the censored name, choosing among 10 different candidate names, in more than 50\% of the investigated cases. This outperforms the results of two reference baselines. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the insidious issues of censorship on the web.

Includes members:
  Member "semi supervised knowledge extraction for detection of drugs and their effects":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "terminologies augmented recurrent neural network model for clinical named entity recognition")
  Member "ossint open source social network intelligence an efficient and effective way to uncover private information in osn profiles":
    also connected through other hyperedges:
      (none)
  Member "science vs conspiracy collective narratives in the age of misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the covid 19 social media infodemic")
  Member "socialspy browsing supposedly hidden information in online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ossint open source social network intelligence an efficient and effective way to uncover private information in osn profiles")
  Member "an automated social graph de anonymization technique":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy in social media identification mitigation and applications")
  Member "the anatomy of web censorship in pakistan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "iclab a global longitudinal internet censorship measurement platform")
  Member "the role of social networks in information diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "information contagion an empirical study of the spread of news on digg and twitter social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling information diffusion in online social networks with partial differential equations")

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)