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: "trick me if you can adversarial writing of trivia challenge questions"
  Abstract: Modern natural language processing systems have been touted as approaching human performance. However, existing datasets are imperfect tests. Examples are written with humans in mind, not computers, and often do not properly expose model limitations. We address this by developing a new process for crowdsourced annotation, adversarial writing, where humans interact with trained models and try to break them. Applying this annotation process to Trivia question answering yields a challenge set, which despite being easy for human players to answer, systematically stumps automated question answering systems. Diagnosing model errors on the evaluation data provides actionable insights to explore in developing more robust and generalizable question answering systems.

Connected through hyperedge HE-1 (induced by source paper "adversarial nli a new benchmark for natural language understanding"):
  with members:
    - "adversarial filters of dataset biases"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "abductive commonsense reasoning"
    - "codah an adversarially authored question answer dataset for common sense"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "multi task deep neural networks for natural language understanding"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"

Connected through hyperedge HE-2 (induced by source paper "quizbowl the case for incremental question answering"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "studio ousia s quiz bowl question answering system"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "a visual analytics framework for adversarial text generation"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "defense methods against adversarial examples for recurrent neural networks"
    - "explaining explanations in ai"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "textbugger generating adversarial text against real world applications"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "detecting and reducing bias in a high stakes domain"):
  with members:
    - "fine grained sentiment analysis with faithful attention"
    - "detecting gang involved escalation on social media using context"
    - "all you need is love evading hate speech detection"
    - "pathologies of neural models make interpretations difficult"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "what can ai do for me evaluating machine learning interpretations in cooperative play"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "on calibration of modern neural networks"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-6 (induced by source paper "interpreting neural networks with nearest neighbors"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "smoothgrad removing noise by adding noise"
    - "on calibration of modern neural networks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "axiomatic attribution for deep networks"
    - "why should i trust you explaining the predictions of any classifier"

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)