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: "secure detection of image manipulation by means of random feature selection"
  Abstract: We address the problem of data-driven image manipulation detection in the presence of an attacker with limited knowledge about the detector. Specifically, we assume that the attacker knows the architecture of the detector, the training data and the class of features V the detector can rely on. In order to get an advantage in his race of arms with the attacker, the analyst designs the detector by relying on a subset of features chosen at random in V. Given its ignorance about the exact feature set, the adversary must attack a version of the detector based on the entire feature set. In this way, the effectiveness of the attack diminishes since there is no guarantee that attacking a detector working in the full feature space will result in a successful attack against the reduced-feature detector. We prove both theoretically and experimentally - by applying the proposed procedure to the detection of two specific kinds of image manipulations - that, thanks to random feature selection, the security of the detector increases significantly at the expense of a negligible loss of performance in the absence of attacks. We theoretically prove that, under some simplifying assumptions, the security of the detector increases significantly thanks to random feature selection. We also provide an experimental validation of the proposed procedure by focusing on the detection of two specific kinds of image manipulations. The experiments confirm the gain in security at the expense of a negligible loss of performance in the absence of attacks.

Connected through hyperedge HE-1 (induced by source paper "automatic discovery of political meme genres with diverse appearances"):
  with members:
    - "dynamic spatial verification for large scale object level image retrieval"
    - "dynamics of team library adoptions an exploration of github commit logs"
    - "needle in a haystack a framework for seeking small objects in big datasets"
    - "fighting fake news image splice detection via learned self consistency"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "multimodal social media analysis for gang violence prevention"
    - "on the origins of memes by means of fringe web communities"
    - "memesequencer sparse matching for embedding image macros"

Connected through hyperedge HE-2 (induced by source paper "effectiveness of random deep feature selection for securing image manipulation detectors against adversarial examples"):
  with members:
    - "defending against adversarial attacks by randomized diversification"
    - "on the transferability of adversarial examples against cnn based image forensics"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "a counter forensic method for cnn based camera model identification"
    - "mitigating adversarial effects through randomization"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-3 (induced by source paper "robustification of deep net classifiers by key based diversified aggregation with pre filtering"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "bridging machine learning and cryptography in defence against adversarial attacks"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "defending against adversarial attacks by randomized diversification"):
  with members:
    - "adversarial noise layer regularize neural network by adding noise"
    - "bridging machine learning and cryptography in defence against adversarial attacks"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "mitigating adversarial effects through randomization"
    - "adversarial examples attacks and defenses for deep learning"
    - "towards evaluating the robustness of neural networks"

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)