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: "gltr statistical detection and visualization of generated text"
  Abstract: The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by and explained to non-experts. We develop GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across common sampling schemes. In a human-subjects study, we show that the annotation scheme provided by GLTR improves the human detection-rate of fake text from 54% to 72% without any prior training. GLTR is open-source and publicly deployed, and has already been widely used to detect generated outputs

Connected through hyperedge HE-1 (induced by source paper "adversarial machine learning perspectives from adversarial risk analysis"):
  with members:
    - "estimating counterfactual treatment outcomes over time through adversarially balanced representations"
    - "gradient methods for solving stackelberg games"
    - "opponent aware reinforcement learning"
    - "knowing when to stop evaluation and verification of conformity to output size specifications"
    - "a survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"

Connected through hyperedge HE-2 (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"
    - "defending against neural fake news"
    - "automated speech generation from un general assembly statements mapping risks in ai generated texts"

Connected through hyperedge HE-3 (induced by source paper "attacking neural text detectors"):
  with members:
    - "release strategies and the social impacts of language models"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "synthesizing robust adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "real or fake learning to discriminate machine from human generated text"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "implicit generation and generalization in energy based models"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "blockwise parallel decoding for deep autoregressive models"

Connected through hyperedge HE-5 (induced by source paper "do massively pretrained language models make better storytellers"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "the curious case of neural text degeneration"
    - "improving neural story generation by targeted common sense grounding"
    - "unifying human and statistical evaluation for natural language generation"
    - "encoder agnostic adaptation for conditional language generation"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"):
  with members:
    - "release strategies and the social impacts of language models"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "neural text generation past present and beyond"
    - "deep text classification can be fooled"
    - "simple fusion return of the language model"
    - "towards diverse text generation with inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "the limitations of stylometry for detecting machine generated fake news"):
  with members:
    - "real or fake learning to discriminate machine from human 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"
    - "real or fake learning to discriminate machine from human generated text"
    - "release strategies and the social impacts of language models"
    - "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.CL(Computation and Language)