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: "image transformation can make neural networks more robust against adversarial examples"
  Abstract: Neural networks are being applied in many tasks related to IoT with encouraging results. For example, neural networks can precisely detect human, objects and animal via surveillance camera for security purpose. However, neural networks have been recently found vulnerable to well-designed input samples that called adversarial examples. Such issue causes neural networks to misclassify adversarial examples that are imperceptible to humans. We found giving a rotation to an adversarial example image can defeat the effect of adversarial examples. Using MNIST number images as the original images, we first generated adversarial examples to neural network recognizer, which was completely fooled by the forged examples. Then we rotated the adversarial image and gave them to the recognizer to find the recognizer to regain the correct recognition. Thus, we empirically confirmed rotation to images can protect pattern recognizer based on neural networks from adversarial example attacks.

Connected through hyperedge HE-1 (induced by source paper "search space of adversarial perturbations against image filters"):
  with members:
    - "automated detection system for adversarial examples with high frequency noises sieve"
    - "magnet a two pronged defense against adversarial examples"
    - "towards evaluating the robustness of neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "towards deep neural network architectures robust to adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-2 (induced by source paper "an efficient pre processing method to eliminate adversarial effects"):
  with members:
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "mitigating adversarial effects through randomization"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-3 (induced by source paper "integrating motion into vision models for better visual prediction"):
  with members:
    - "saccadic predictive vision model with a fovea"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "unsupervised learning from continuous video in a scalable predictive recurrent network"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "recurrent models of visual attention"
    - "playing atari with deep reinforcement learning"

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)