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: "release strategies and the social impacts of language models"
  Abstract: Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more. However, their flexibility and generative capabilities also raise misuse concerns. This report discusses OpenAI's work related to the release of its GPT-2 language model. It discusses staged release, which allows time between model releases to conduct risk and benefit analyses as model sizes increased. It also discusses ongoing partnership-based research and provides recommendations for better coordination and responsible publication in AI.

Connected through hyperedge HE-1 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements"):
  with members:
    - "plug and play language models a simple approach to controlled text generation"
    - "generalizing from a few examples a survey on few shot learning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "defending against neural fake news"
    - "studying fake news via network analysis detection and mitigation"
    - "hierarchical propagation networks for fake news detection investigation and exploitation"
    - "declare debunking fake news and false claims using evidence aware deep learning"

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"
    - "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-3 (induced by source paper "attacking neural text detectors"):
  with members:
    - "gltr statistical detection and visualization of generated text"
    - "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 "reducing sentiment bias in language models via counterfactual evaluation"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "certified robustness to adversarial word substitutions"
    - "multi task deep neural networks for natural language understanding"
    - "reducing gender bias in word level language models with a gender equalizing loss function"
    - "transformer xl attentive language models beyond a fixed length context"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-5 (induced by source paper "defending against neural fake news"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "mask predict parallel decoding of conditional masked language models"
    - "unsupervised domain adaptation of contextualized embeddings for sequence labeling"
    - "from recognition to cognition visual commonsense reasoning"

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:
    - "gltr statistical detection and visualization of generated text"
    - "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 "forbidden knowledge in machine learning reflections on the limits of research and publication"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "artificial intelligence governance and ethics global perspectives"
    - "human detection of machine manipulated media"
    - "text based editing of talking head video"
    - "brainnet a multi person brain to brain interface for direct collaboration between brains"
    - "a replication study machine learning models are capable of predicting sexual orientation from facial images"
    - "reducing malicious use of synthetic media research considerations and potential release practices for machine learning"

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"
    - "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.CL(Computation and Language)