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: "attacking visual language grounding with adversarial examples a case study on neural image captioning"
  Abstract: Visual language grounding is widely studied in modern neural image captioning systems, which typically adopts an encoder-decoder framework consisting of two principal components: a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) for language caption generation. To study the robustness of language grounding to adversarial perturbations in machine vision and perception, we propose Show-and-Fool, a novel algorithm for crafting adversarial examples in neural image captioning. The proposed algorithm provides two evaluation approaches, which check whether neural image captioning systems can be mislead to output some randomly chosen captions or keywords. Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems. Consequently, our approach leads to new robustness implications of neural image captioning and novel insights in visual language grounding.

Connected through hyperedge HE-1 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-2 (induced by source paper "adversarial robustness vs model compression or both"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "a direct approach to robust deep learning using adversarial networks"
    - "defensive quantization when efficiency meets robustness"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "sparse dnns with improved adversarial robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-3 (induced by source paper "fda feature disruptive attack"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "adversarial logit pairing"
    - "gray box adversarial training"
    - "deflecting adversarial attacks with pixel deflection"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "towards stable and efficient training of verifiably robust neural networks"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "certified adversarial robustness via randomized smoothing"
    - "a provable defense for deep residual networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "verification of non linear specifications for neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-5 (induced by source paper "unsupervised adversarial attacks on deep feature based retrieval with gan"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "generating adversarial examples with adversarial networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "visual search at ebay"
    - "fine tuning cnn image retrieval with no human annotation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "a case study on combining asr and visual features for generating instructional video captions"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "multimodal abstractive summarization for how2 videos"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "learning to localize and align fine grained actions to sparse instructions"
    - "a neural compositional paradigm for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "audio visual event localization in unconstrained videos"

Connected through hyperedge HE-7 (induced by source paper "robust decision trees against adversarial examples"):
  with members:
    - "gradient regularized budgeted boosting"
    - "theoretically principled trade off between robustness and accuracy"
    - "multi layered gradient boosting decision trees"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "scaling provable adversarial defenses"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "mixtrain scalable training of formally robust neural networks"

Connected through hyperedge HE-8 (induced by source paper "paws paraphrase adversaries from word scrambling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "assessing composition in sentence vector representations"
    - "gender bias in coreference resolution"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "open subtitles paraphrase corpus for six languages"

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.CV(Computer Vision and Pattern Recognition)