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: "fooling vision and language models despite localization and attention mechanism"
  Abstract: Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question answering (VQA) models. Our evaluation shows that we can generate adversarial examples with a high success rate (i.e., > 90%) for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses.

Connected through hyperedge HE-1 (induced by source paper "cycle consistency for robust visual question answering"):
  with members:
    - "bilinear attention networks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "the natural language decathlon multitask learning as question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "ivqa inverse visual question answering"

Connected through hyperedge HE-2 (induced by source paper "deep ordinal hashing with spatial attention"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "end to end comparative attention networks for person re identification"
    - "deep region hashing for efficient large scale instance search from images"
    - "supervising neural attention models for video captioning by human gaze data"
    - "image captioning with semantic attention"
    - "weakly supervised cascaded convolutional networks"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-3 (induced by source paper "on the robustness of human pose estimation"):
  with members:
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "generating adversarial examples with adversarial networks"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "upset and angri breaking high performance image classifiers"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "generative adversarial perturbations"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-4 (induced by source paper "mimic and fool a task agnostic adversarial attack"):
  with members:
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "i revnet deep invertible networks"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "learning to reason end to end module networks for visual question answering"
    - "adversarial examples for semantic segmentation and object detection"
    - "practical black box attacks against machine learning"
    - "delving into transferable adversarial examples and black box attacks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-5 (induced by source paper "sunny and dark outside improving answer consistency in vqa through entailed question generation"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "cycle consistency for robust visual question answering"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "evaluating quality of chatbots and intelligent conversational agents"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "learning by asking questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-6 (induced by source paper "exploring the robustness of nmt systems to nonsensical inputs"):
  with members:
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "on adversarial examples for character level neural machine translation"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "detecting egregious responses in neural sequence to sequence models"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "identify susceptible locations in medical records via adversarial attacks on deep predictive models"
    - "spatially transformed adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "generating adversarial examples with adversarial networks"
    - "query efficient hard label black box attack an optimization based approach"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-8 (induced by source paper "backdooring convolutional neural networks via targeted weight perturbations"):
  with members:
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "robust physical world attacks on deep learning models"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "universal adversarial perturbations"

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.AI(Artificial Intelligence)