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: "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"
  Abstract: Advanced neural language models (NLMs) are widely used in sequence generation tasks because they are able to produce fluent and meaningful sentences. They can also be used to generate fake reviews, which can then be used to attack online review systems and influence the buying decisions of online shoppers. To perform such attacks, it is necessary for experts to train a tailored LM for a specific topic. In this work, we show that a low-skilled threat model can be built just by combining publicly available LMs and show that the produced fake reviews can fool both humans and machines. In particular, we use the GPT-2 NLM to generate a large number of high-quality reviews based on a review with the desired sentiment and then using a BERT based text classifier (with accuracy of 96%) to filter out reviews with undesired sentiments. Because none of the words in the review are modified, fluent samples like the training data can be generated from the learned distribution. A subjective evaluation with 80 participants demonstrated that this simple method can produce reviews that are as fluent as those written by people. It also showed that the participants tended to distinguish fake reviews randomly. Three countermeasures, Grover, GLTR, and OpenAI GPT-2 detector, were found to be difficult to accurately detect fake review.

Connected through hyperedge HE-1 (induced by source paper "deceptive ai explanations creation and detection"):
  with members:
    - "human grounded evaluations of explanation methods for text classification"
    - "a survey on bias and fairness in machine learning"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "how to manipulate cnns to make them lie the gradcam case"
    - "how model accuracy and explanation fidelity influence user trust"
    - "perturbation sensitivity analysis to detect unintended model biases"
    - "rise randomized input sampling for explanation of black box models"
    - "sanity checks for saliency maps"

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"
    - "are we safe yet the limitations of distributional features for fake news detection"

Connected through hyperedge HE-3 (induced by source paper "release strategies and the social impacts of language models"):
  with members:
    - "real or fake learning to discriminate machine from human generated text"
    - "clinically accurate chest x ray report generation"
    - "defending against neural fake news"
    - "semi supervised sequence learning"

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)