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: "scratch that an evolution based adversarial attack against neural networks"
  Abstract: Recent research has shown that Deep Neural Networks (DNNs) for image classification are vulnerable to adversarial attacks. However, most works on adversarial samples utilize sub-perceptual noise that, while invisible or slightly visible to humans, often covers the entire image. Additionally, most of these attacks often require knowledge of the neural network architecture and its parameters, and the ability to calculate the gradients of the parameters with respect to the inputs. In this work, we show that it is possible to attack neural networks in a highly restricted threat setting, where attackers have no knowledge of the neural network (i.e., in a black-box setting) and can only modify highly localized adversarial noise in the form of randomly chosen straight lines or scratches. Our Adversarial Scratches attack method covers only 1-2% of the image pixels and are generated using the Covariance Matrix Adaptation Evolutionary Strategy, a purely black-box method that does not require knowledge of the neural network architecture and its gradients. Against ImageNet models, Adversarial Scratches requires 3 times fewer queries than GenAttack (without any optimizations) and 73 times fewer queries than ZOO, both prior state-of-the-art black-box attacks. We successfully deceive state-of-the-art Inception-v3, ResNet-50 and VGG-19 models trained on ImageNet with deceiving rates of 75.8%, 62.7%, and 45% respectively, with fewer queries than several state-of-the-art black-box attacks, while modifying less than 2% of the image pixels. Additionally, we provide a new threat scenario for neural networks, demonstrate a new attack surface that can be used to perform adversarial attacks, and discuss its potential implications.

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.NE(Neural and Evolutionary Computing)