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: "deepfense online accelerated defense against adversarial deep learning"
  Abstract: Recent advances in adversarial Deep Learning (DL) have opened up a largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. With the wide-spread usage of DL in critical and time-sensitive applications, including unmanned vehicles, drones, and video surveillance systems, online detection of malicious inputs is of utmost importance. We propose DeepFense, the first end-to-end automated framework that simultaneously enables efficient and safe execution of DL models. DeepFense formalizes the goal of thwarting adversarial attacks as an optimization problem that minimizes the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint modular redundancies are trained to validate the legitimacy of the input samples in parallel with the victim DL model. DeepFense leverages hardware/software/algorithm co-design and customized acceleration to achieve just-in-time performance in resource-constrained settings. The proposed countermeasure is unsupervised, meaning that no adversarial sample is leveraged to train modular redundancies. We further provide an accompanying API to reduce the non-recurring engineering cost and ensure automated adaptation to various platforms. Extensive evaluations on FPGAs and GPUs demonstrate up to two orders of magnitude performance improvement while enabling online adversarial sample detection.

Connected through hyperedge HE-1 (induced by source paper "detection of adversarial attacks and characterization of adversarial subspace"):
  with members:
    - "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "on the statistical detection of adversarial examples"
    - "adversarial diversity and hard positive generation"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-2 (induced by source paper "principal component properties of adversarial samples"):
  with members:
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "towards robust detection of adversarial examples"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "deep residual learning for image recognition"
    - "the limitations of deep learning in adversarial settings"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "muldef multi model based defense against adversarial examples for neural networks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "dense associative memory is robust to adversarial inputs"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "magnet a two pronged defense against adversarial examples"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "efficient defenses against adversarial attacks"
    - "simple black box adversarial perturbations for deep networks"

Connected through hyperedge HE-4 (induced by source paper "characterizing adversarial subspaces using local intrinsic dimensionality"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "on the statistical detection of adversarial examples"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

Connected through hyperedge HE-5 (induced by source paper "blackmarks black box multi bit watermarking for deep neural networks"):
  with members:
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "embedding watermarks into deep neural networks"
    - "adversarial frontier stitching for remote neural network watermarking"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "wide residual 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)