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: "text processing like humans do visually attacking and shielding nlp systems"
  Abstract: Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual input perturbations demonstrate. We then investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82\%. We then explore three shielding methods---visual character embeddings, adversarial training, and rule-based recovery---which substantially improve the robustness of the models. However, the shielding methods still fall behind performances achieved in non-attack scenarios, which demonstrates the difficulty of dealing with visual attacks.

Connected through hyperedge HE-1 (induced by source paper "g odel s sentence is an adversarial example but unsolvable"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "disentangling adversarial robustness and generalization"

Connected through hyperedge HE-2 (induced by source paper "moverscore text generation evaluating with contextualized embeddings and earth mover distance"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards scalable and reliable capsule networks for challenging nlp applications"
    - "linguistic knowledge and transferability of contextual representations"
    - "better rewards yield better summaries learning to summarise without references"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a simple theoretical model of importance for summarization"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "a visual analytics framework for adversarial text generation"):
  with members:
    - "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"
    - "fine tuned language models for text classification"

Connected through hyperedge HE-4 (induced by source paper "towards scalable and reliable capsule networks for challenging nlp applications"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "zero shot user intent detection via capsule neural networks"
    - "towards linear time neural machine translation with capsule networks"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "dynamic routing between capsules"
    - "attentive pooling networks"
    - "simple question answering by attentive convolutional neural network"

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)