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: "combating adversarial misspellings with robust word recognition"
  Abstract: To combat adversarial spelling mistakes, we propose placing a word recognition model in front of the downstream classifier. Our word recognition models build upon the RNN semi-character architecture, introducing several new backoff strategies for handling rare and unseen words. Trained to recognize words corrupted by random adds, drops, swaps, and keyboard mistakes, our method achieves 32% relative (and 3.3% absolute) error reduction over the vanilla semi-character model. Notably, our pipeline confers robustness on the downstream classifier, outperforming both adversarial training and off-the-shelf spell checkers. Against a BERT model fine-tuned for sentiment analysis, a single adversarially-chosen character attack lowers accuracy from 90.3% to 45.8%. Our defense restores accuracy to 75%. Surprisingly, better word recognition does not always entail greater robustness. Our analysis reveals that robustness also depends upon a quantity that we denote the sensitivity.

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"
    - "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"
    - "probing neural network comprehension of natural language arguments"

Connected through hyperedge HE-2 (induced by source paper "robustness to programmable string transformations via augmented abstract training"):
  with members:
    - "popqorn quantifying robustness of recurrent neural networks"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "generating natural adversarial examples"
    - "robsut wrod reocginiton via semi character recurrent neural network"
    - "adam a method for stochastic optimization"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples for models of code"):
  with members:
    - "gnn film graph neural networks with feature wise linear modulation"
    - "when deep learning met code search"
    - "getafix learning to fix bugs automatically"
    - "structural language models for any code generation"
    - "code2vec learning distributed representations of code"
    - "targeted adversarial examples for black box audio systems"
    - "a general path based representation for predicting program properties"
    - "did you hear that adversarial examples against automatic speech recognition"

Connected through hyperedge HE-4 (induced by source paper "certified robustness to adversarial word substitutions"):
  with members:
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "training verified learners with learned verifiers"
    - "generating natural language adversarial examples"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-5 (induced by source paper "natural language adversarial attacks and defenses in word level"):
  with members:
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "improving the generalization of adversarial training with domain adaptation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "shielding google s language toxicity model against adversarial attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "hotflip white box adversarial examples for text classification"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-6 (induced by source paper "learning multi level dependencies for robust word recognition"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "build it break it fix it for dialogue safety robustness from adversarial human attack"
    - "say what i want towards the dark side of neural dialogue models"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "near human level performance in grammatical error correction with hybrid machine translation"

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)