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 authentication based on neural networks"
  Abstract: Neural network has been attracting more and more researchers since the past decades. The properties, such as parameter sensitivity, random similarity, learning ability, etc., make it suitable for information protection, such as data encryption, data authentication, intrusion detection, etc. In this paper, by investigating neural networks' properties, the low-cost authentication method based on neural networks is proposed and used to authenticate images or videos. The authentication method can detect whether the images or videos are modified maliciously. Firstly, this chapter introduces neural networks' properties, such as parameter sensitivity, random similarity, diffusion property, confusion property, one-way property, etc. Secondly, the chapter gives an introduction to neural network based protection methods. Thirdly, an image or video authentication scheme based on neural networks is presented, and its performances, including security, robustness and efficiency, are analyzed. Finally, conclusions are drawn, and some open issues in this field are presented.

Connected through hyperedge HE-1 (induced by source paper "feedback learning for improving the robustness of neural networks"):
  with members:
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "boosting adversarial attacks with momentum"
    - "mitigating evasion attacks to deep neural networks via region based classification"
    - "adversarial examples attacks and defenses for deep learning"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "image based vehicle analysis using deep neural network a systematic study"

Connected through hyperedge HE-2 (induced by source paper "a multi strength adversarial training method to mitigate adversarial attacks"):
  with members:
    - "vehicle classification using transferable deep neural network features"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "fractional max pooling"
    - "intriguing properties of neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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.MM(Multimedia)