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: "pathologies of neural models make interpretations difficult"
  Abstract: One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence. In existing interpretation methods for NLP, a word's importance is determined by either input perturbation---measuring the decrease in model confidence when that word is removed---or by the gradient with respect to that word. To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input. This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods. As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence. To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood. To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples. Fine-tuned models become more interpretable under input reduction without accuracy loss on regular examples.

Connected through hyperedge HE-1 (induced by source paper "overinterpretation reveals image classification model pathologies"):
  with members:
    - "gradmask reduce overfitting by regularizing saliency"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "a benchmark for interpretability methods in deep neural networks"
    - "image synthesis with a single robust classifier"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "underwhelming generalization improvements from controlling feature attribution"
    - "probing neural network comprehension of natural language arguments"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "eraser a benchmark to evaluate rationalized nlp models"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "attention is not not explanation"
    - "on identifiability in transformers"
    - "inferring which medical treatments work from reports of clinical trials"
    - "fine grained sentiment analysis with faithful attention"
    - "on the validity of self attention as explanation in transformer models"
    - "rethinking cooperative rationalization introspective extraction and complement control"
    - "is attention interpretable"

Connected through hyperedge HE-3 (induced by source paper "is bert really robust a strong baseline for natural language attack on text classification and entailment"):
  with members:
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "probing neural network comprehension of natural language arguments"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "universal sentence encoder"

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"
    - "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"
    - "deriving machine attention from human rationales"

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)