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: "universal adversarial triggers for attacking and analyzing nlp"
  Abstract: Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of "why" questions in SQuAD to be answered "to kill american people", and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "episodic memory in lifelong language learning"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "visualizing and measuring the geometry of bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "emerging cross lingual structure in pretrained language models"
    - "bert rediscovers the classical nlp pipeline"
    - "unsupervised cross lingual representation learning at scale"
    - "q bert hessian based ultra low precision quantization of bert"

Connected through hyperedge HE-2 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning"):
  with members:
    - "towards a robust deep neural network in text domain a survey"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "understanding the origins of bias in word embeddings"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "towards understanding linear word analogies"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions"):
  with members:
    - "gated group self attention for answer selection"
    - "interpretable question answering on knowledge bases and text"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "on the validity of self attention as explanation in transformer models"
    - "is attention interpretable"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "on variational bounds of mutual information"
    - "entropy and mutual information in models of deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "measurement and fairness"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an intersectional definition of fairness"

Connected through hyperedge HE-5 (induced by source paper "hierarchical reinforcement learning for open domain dialog"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "evaluating coherence in dialogue systems using entailment"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "team qcri mit at semeval 2019 task 4 propaganda analysis meets hyperpartisan news detection"

Connected through hyperedge HE-6 (induced by source paper "adversarial language games for advanced natural language intelligence"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "egg a toolkit for research on emergence of language in games"
    - "robust neural machine translation with doubly adversarial inputs"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "robust machine comprehension models via adversarial training"
    - "planning inference and pragmatics in sequential language games"

Connected through hyperedge HE-7 (induced by source paper "plug and play language models a simple approach to controlled text generation"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "the curious case of neural text degeneration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fine tuning language models from human preferences"
    - "putting machine translation in context with the noisy channel model"

Connected through hyperedge HE-8 (induced by source paper "what does bert learn from multiple choice reading comprehension datasets"):
  with members:
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "albert a lite bert for self supervised learning of language representations"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"

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)