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 "human and automatic detection of generated text"):
  Abstract: With the advent of generative models with a billion parameters or more, it is now possible to automatically generate vast amounts of human-sounding text. This raises questions into just how human-like is the machine-generated text, and how long does a text excerpt need to be for both humans and automatic discriminators to be able reliably detect that it was machine-generated. In this paper, we conduct a thorough investigation of how choices such as sampling strategy and text excerpt length can impact the performance of automatic detection methods as well as human raters. We find that the sampling strategies which result in more human-like text according to human raters create distributional differences from human-written text that make detection easy for automatic discriminators.

Includes members:
  Member "the curious case of neural text degeneration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "do massively pretrained language models make better storytellers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "bertscore evaluating text generation with bert":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "real or fake learning to discriminate machine from human generated text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limits of detecting text generated by large scale language models")
  Member "release strategies and the social impacts of language models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "gltr statistical detection and visualization of generated text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial machine learning perspectives from adversarial risk analysis")
  Member "defending against neural fake news":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "are we safe yet the limitations of distributional features for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "limits of detecting text generated by large scale language models")

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)