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: "spoofing 2d face detection machines see people who aren t there"
  Abstract: Machine learning is increasingly used to make sense of the physical world yet may suffer from adversarial manipulation. We examine the Viola-Jones 2D face detection algorithm to study whether images can be created that humans do not notice as faces yet the algorithm detects as faces. We show that it is possible to construct images that Viola-Jones recognizes as containing faces yet no human would consider a face. Moreover, we show that it is possible to construct images that fool facial detection even when they are printed and then photographed.

Connected through hyperedge HE-1 (induced by source paper "adversarial perturbations against real time video classification systems"):
  with members:
    - "drive video analysis for the detection of traffic near miss incidents"
    - "nag network for adversary generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "enhancing robustness of machine learning systems via data transformations"):
  with members:
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "deceiving google s cloud video intelligence api built for summarizing videos"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "early methods for detecting adversarial images"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "a study of the effect of jpg compression on adversarial images"
    - "foveation based mechanisms alleviate 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.CR(Cryptography and Security)