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 patch"
  Abstract: We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class. These adversarial patches can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class.  To reproduce the results from the paper, our code is available at https://github.com/tensorflow/cleverhans/tree/master/examples/adversarial_patch

Connected through hyperedge HE-1 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "on evaluating adversarial robustness"
    - "model reuse attacks on deep learning systems"
    - "yolov3 an incremental improvement"
    - "lavan localized and visible adversarial noise"
    - "anomaly detection using one class neural networks"
    - "review of deep learning"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-2 (induced by source paper "a little fog for a large turn"):
  with members:
    - "natural adversarial examples"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "exploring the limits of weakly supervised pretraining"
    - "idd a dataset for exploring problems of autonomous navigation in unconstrained environments"
    - "audio adversarial examples targeted attacks on speech to text"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-3 (induced by source paper "graph universal adversarial attacks a few bad actors ruin graph learning models"):
  with members:
    - "session based recommendation with graph neural network"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "adversarial attacks on graph neural networks via meta learning"
    - "universal transferable and targeted adversarial attacks"
    - "adversarial attack on graph structured data"
    - "attack graph convolutional networks by adding fake nodes"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-4 (induced by source paper "adversarial machine learning perspectives from adversarial risk analysis"):
  with members:
    - "estimating counterfactual treatment outcomes over time through adversarially balanced representations"
    - "gradient methods for solving stackelberg games"
    - "opponent aware reinforcement learning"
    - "knowing when to stop evaluation and verification of conformity to output size specifications"
    - "gltr statistical detection and visualization of generated text"
    - "a survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-5 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient"):
  with members:
    - "a comprehensive analysis on adversarial robustness of spiking neural networks"
    - "snn under attack are spiking deep belief networks vulnerable to adversarial examples"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "direct training for spiking neural networks faster larger better"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-6 (induced by source paper "attacking optical character recognition ocr systems with adversarial watermarks"):
  with members:
    - "smooth adversarial examples"
    - "harmonic adversarial attack method"
    - "lavan localized and visible adversarial noise"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "on the statistical detection of adversarial examples"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-7 (induced by source paper "query efficient physical hard label attacks on deep learning visual classification"):
  with members:
    - "sign opt a query efficient hard label adversarial attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "synthesizing robust adversarial examples"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-8 (induced by source paper "detecting patch adversarial attacks with image residuals"):
  with members:
    - "de randomized smoothing for certifiable defense against patch attacks"
    - "certified defenses for adversarial patches"
    - "one pixel attack for fooling deep neural networks"
    - "a fourier perspective on model robustness in computer vision"
    - "sentinet detecting physical attacks against deep learning systems"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lavan localized and visible adversarial noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

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)