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: "are we safe yet the limitations of distributional features for fake news detection"
  Abstract: Automatic detection of fake news --- texts that are deceitful and misleading --- is a long outstanding and largely unsolved problem. Worse yet, recent developments in language modeling allow for the automatic generation of such texts. One approach that has recently gained attention detects these fake news using stylometry-based provenance, i.e. tracing a text's writing style back to its producing source and determining whether the source is malicious. This was shown to be highly effective under the assumption that legitimate text is produced by humans, and fake text is produced by a language model. #R##N#In this work, we identify a fundamental problem with provenance-based approaches against attackers that auto-generate fake news: fake and legitimate texts can originate from nearly identical sources. First, a legitimate text might be auto-generated in a similar process to that of fake text, and second, attackers can automatically corrupt articles originating from legitimate human sources. We demonstrate these issues by simulating attacks in such settings, and find that the provenance approach fails to defend against them. Our findings highlight the importance of assessing the veracity of the text rather than solely relying on its style or source. We also open up a discussion on the types of benchmarks that should be used to evaluate neural fake news detectors.

Connected through hyperedge HE-1 (induced by source paper "limits of detecting text generated by large scale language models"):
  with members:
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"

Connected through hyperedge HE-2 (induced by source paper "human and automatic detection of generated text"):
  with members:
    - "the curious case of neural text degeneration"
    - "do massively pretrained language models make better storytellers"
    - "bertscore evaluating text generation with bert"
    - "real or fake learning to discriminate machine from human generated text"
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"

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)