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: "adversarial attacks on face detectors using neural net based constrained optimization"
  Abstract: Adversarial attacks involve adding, small, often imperceptible, perturbations to inputs with the goal of getting a machine learning model to misclassifying them. While many different adversarial attack strategies have been proposed on image classification models, object detection pipelines have been much harder to break. In this paper, we propose a novel strategy to craft adversarial examples by solving a constrained optimization problem using an adversarial generator network. Our approach is fast and scalable, requiring only a forward pass through our trained generator network to craft an adversarial sample. Unlike in many attack strategies, we show that the same trained generator is capable of attacking new images without explicitly optimizing on them. We evaluate our attack on a trained Faster R-CNN face detector on the cropped 300-W face dataset where we manage to reduce the number of detected faces to $0.5\%$ of all originally detected faces. In a different experiment, also on 300-W, we demonstrate the robustness of our attack to a JPEG compression based defense typical JPEG compression level of $75\%$ reduces the effectiveness of our attack from only $0.5\%$ of detected faces to a modest $5.0\%$.

Connected through hyperedge HE-1 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "robust adversarial perturbation on deep proposal based models"

Connected through hyperedge HE-2 (induced by source paper "advjnd generating adversarial examples with just noticeable difference"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "perceptual evaluation of adversarial attacks for cnn based image classification"
    - "a general framework for adversarial examples with objectives"
    - "black box attacks against rnn based malware detection algorithms"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "densely connected convolutional networks"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-3 (induced by source paper "face off adversarial face obfuscation"):
  with members:
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "generating adversarial examples with adversarial networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mitigating evasion attacks to deep neural networks via region based classification"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-4 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "attacking optical flow"
    - "a general framework for adversarial examples with objectives"
    - "on the in fidelity and sensitivity for explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"

Connected through hyperedge HE-5 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "pyramidbox a context assisted single shot face detector"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-6 (induced by source paper "poba ga perturbation optimized black box adversarial attacks via genetic algorithm"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "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"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "asp a fast adversarial attack example generation framework based on adversarial saliency prediction"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-7 (induced by source paper "advfaces adversarial face synthesis"):
  with members:
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "a general framework for adversarial examples with objectives"
    - "spatially transformed adversarial examples"
    - "arcface additive angular margin loss for deep face recognition"
    - "fast geometrically perturbed adversarial faces"
    - "attacks on state of the art face recognition using attentional adversarial attack generative network"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"

Connected through hyperedge HE-8 (induced by source paper "amora black box adversarial morphing attack"):
  with members:
    - "fakelocator robust localization of gan based face manipulations via semantic segmentation networks with bells and whistles"
    - "advfaces adversarial face synthesis"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "attacking optical flow"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "wasserstein smoothing certified robustness against wasserstein adversarial attacks"
    - "machine learning testing survey landscapes and horizons"

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)