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: "real or fake learning to discriminate machine from human generated text"
  Abstract: Recent advances in generative modeling of text have demonstrated remarkable improvements in terms of fluency and coherency. In this work we investigate to which extent a machine can discriminate real from machine generated text. This is important in itself for automatic detection of computer generated stories, but can also serve as a tool for further improving text generation. We show that learning a dedicated scoring function to discriminate between real and fake text achieves higher precision than employing the likelihood of a generative model. The scoring functions generalize to other generators than those used for training as long as these generators have comparable model complexity and are trained on similar datasets.

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"
    - "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"
    - "automated speech generation from un general assembly statements mapping risks in ai generated texts"

Connected through hyperedge HE-2 (induced by source paper "style example guided text generation using generative adversarial transformers"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "ctrl a conditional transformer language model for controllable generation"
    - "pretraining based natural language generation for text summarization"
    - "multi task deep neural networks for natural language understanding"
    - "domain adaptive text style transfer"
    - "defending against neural fake news"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-3 (induced by source paper "the dialogue dodecathlon open domain knowledge and image grounded conversational agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "lxmert learning cross modality encoder representations from transformers"
    - "the curious case of neural text degeneration"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "ctrl a conditional transformer language model for controllable generation"

Connected through hyperedge HE-4 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "mass masked sequence to sequence pre training for language generation"
    - "negative training for neural dialogue response generation"

Connected through hyperedge HE-5 (induced by source paper "all in one image grounded conversational agents"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "towards vqa models that can read"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "12 in 1 multi task vision and language representation learning"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"

Connected through hyperedge HE-6 (induced by source paper "how decoding strategies affect the verifiability of generated text"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "language models as knowledge bases"
    - "investigating entity knowledge in bert with simple neural end to end entity linking"
    - "mixture content selection for diverse sequence generation"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "the curious case of neural text degeneration"
    - "tmlab generative enhanced model gem for adversarial attacks"
    - "ctrl a conditional transformer language model for controllable generation"

Connected through hyperedge HE-7 (induced by source paper "the limitations of stylometry for detecting machine generated fake news"):
  with members:
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "the language of legal and illegal activity on the darknet"
    - "all in one multi task learning for rumour verification"
    - "the fact extraction and verification fever shared task"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "fake news detection in social networks via crowd signals"

Connected through hyperedge HE-8 (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"
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "are we safe yet the limitations of distributional features for fake news detection"
    - "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.LG(Machine Learning)