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: "black box adversarial attacks on video recognition models"
  Abstract: Deep neural networks (DNNs) are known for their vulnerability to adversarial examples. These are examples that have undergone small, carefully crafted perturbations, and which can easily fool a DNN into making misclassifications at test time. Thus far, the field of adversarial research has mainly focused on image models, under either a white-box setting, where an adversary has full access to model parameters, or a black-box setting where an adversary can only query the target model for probabilities or labels. Whilst several white-box attacks have been proposed for video models, black-box video attacks are still unexplored. To close this gap, we propose the first black-box video attack framework, called V-BAD. V-BAD utilizes tentative perturbations transferred from image models, and partition-based rectifications found by the NES on partitions (patches) of tentative perturbations, to obtain good adversarial gradient estimates with fewer queries to the target model. V-BAD is equivalent to estimating the projection of an adversarial gradient on a selected subspace. Using three benchmark video datasets, we demonstrate that V-BAD can craft both untargeted and targeted attacks to fool two state-of-the-art deep video recognition models. For the targeted attack, it achieves $>$93\% success rate using only an average of $3.4 \sim 8.4 \times 10^4$ queries, a similar number of queries to state-of-the-art black-box image attacks. This is despite the fact that videos often have two orders of magnitude higher dimensionality than static images. We believe that V-BAD is a promising new tool to evaluate and improve the robustness of video recognition models to black-box adversarial attacks.

Connected through hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior"):
  with members:
    - "sparse black box video attack with reinforcement learning"
    - "heuristic black box adversarial attacks on video recognition models"
    - "deep video inpainting"
    - "appending adversarial frames for universal video attack"
    - "sequence level semantics aggregation for video object detection"
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "sparse adversarial perturbations for videos"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-2 (induced by source paper "skip connections matter on the transferability of adversarial examples generated with resnets"):
  with members:
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "one pixel attack for fooling deep neural networks"
    - "enhancing adversarial example transferability with an intermediate level attack"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "black box adversarial attacks with limited queries and information"
    - "sparsefool a few pixels make a big difference"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-3 (induced by source paper "adversarial camouflage hiding physical world attacks with natural styles"):
  with members:
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "adversarial t shirt evading person detectors in a physical world"
    - "semantic adversarial examples"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-4 (induced by source paper "clean label backdoor attacks on video recognition models"):
  with members:
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "spectral signatures in backdoor attacks"
    - "robust physical world attacks on deep learning models"
    - "certified defenses for data poisoning attacks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"

Connected through hyperedge HE-5 (induced by source paper "sparse black box video attack with reinforcement learning"):
  with members:
    - "identifying and resisting adversarial videos using temporal consistency"
    - "sparse adversarial perturbations for videos"
    - "black box adversarial attacks with limited queries and information"
    - "adversarial perturbations against real time video classification systems"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "deep reinforcement learning an overview"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "understanding adversarial attacks on deep learning based medical image analysis systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "one pixel attack for fooling deep neural networks"
    - "interpreting and improving adversarial robustness with neuron sensitivity"
    - "towards noise robust neural networks via progressive adversarial training"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"

Connected through hyperedge HE-7 (induced by source paper "appending adversarial frames for universal video attack"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "sparse adversarial perturbations for videos"
    - "adversarial perturbations against real time video classification systems"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "adversarial examples for semantic segmentation and object detection"

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.LG(Machine Learning)