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: "adversarially regularising neural nli models to integrate logical background knowledge"
  Abstract: Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake. They are useful for understanding the shortcomings of machine learning models, interpreting their results, and for regularisation. In NLP, however, most example generation strategies produce input text by using known, pre-specified semantic transformations, requiring significant manual effort and in-depth understanding of the problem and domain. In this paper, we investigate the problem of automatically generating adversarial examples that violate a set of given First-Order Logic constraints in Natural Language Inference (NLI). We reduce the problem of identifying such adversarial examples to a combinatorial optimisation problem, by maximising a quantity measuring the degree of violation of such constraints and by using a language model for generating linguistically-plausible examples. Furthermore, we propose a method for adversarially regularising neural NLI models for incorporating background knowledge. Our results show that, while the proposed method does not always improve results on the SNLI and MultiNLI datasets, it significantly and consistently increases the predictive accuracy on adversarially-crafted datasets -- up to a 79.6% relative improvement -- while drastically reducing the number of background knowledge violations. Furthermore, we show that adversarial examples transfer among model architectures, and that the proposed adversarial training procedure improves the robustness of NLI models to adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "stancy stance classification based on consistency cues"
    - "multi task deep neural networks for natural language understanding"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "combating adversarial misspellings with robust word recognition"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "sampling bias in deep active classification an empirical study"

Connected through hyperedge HE-2 (induced by source paper "analyzing differentiable fuzzy logic operators"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "semi supervised learning using differentiable reasoning"
    - "injecting prior knowledge for transfer learning into reinforcement learning algorithms using logic tensor networks"
    - "learning and t norms theory"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "deepproblog neural probabilistic logic programming"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "the web as a knowledge base for answering complex questions"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-4 (induced by source paper "learning credible deep neural networks with rationale regularization"):
  with members:
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "investigating backtranslation in neural machine translation"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "techniques for interpretable machine learning"

Connected through hyperedge HE-5 (induced by source paper "don t take the premise for granted mitigating artifacts in natural language inference"):
  with members:
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "on adversarial examples for character level neural machine translation"
    - "annotation artifacts in natural language inference data"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "analysis methods in neural language processing a survey"
    - "adventure adversarial training for textual entailment with knowledge guided examples"

Connected through hyperedge HE-6 (induced by source paper "augmenting neural networks with first order logic"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "backpropagating through structured argmax using a spigot"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "towards semi supervised learning for deep semantic role labeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to generate reviews and discovering sentiment"

Connected through hyperedge HE-7 (induced by source paper "undersensitivity in neural reading comprehension"):
  with members:
    - "learning to ask unanswerable questions for machine reading comprehension"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-8 (induced by source paper "a logic driven framework for consistency of neural models"):
  with members:
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "augmenting neural networks with first order logic"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

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.LG(Machine Learning)