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: "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
  Abstract: Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on model generalizability and new threats such as prediction-evasive misclassification or stealthy reprogramming. Among different defense proposals, stochastic network defenses such as random neuron activation pruning or random perturbation to layer inputs are shown to be promising for attack mitigation. However, one critical drawback of current defenses is that the robustness enhancement is at the cost of noticeable performance degradation on legitimate data, e.g., large drop in test accuracy. This paper is motivated by pursuing for a better trade-off between adversarial robustness and test accuracy for stochastic network defenses. We propose Defense Efficiency Score (DES), a comprehensive metric that measures the gain in unsuccessful attack attempts at the cost of drop in test accuracy of any defense. To achieve a better DES, we propose hierarchical random switching (HRS), which protects neural networks through a novel randomization scheme. A HRS-protected model contains several blocks of randomly switching channels to prevent adversaries from exploiting fixed model structures and parameters for their malicious purposes. Extensive experiments show that HRS is superior in defending against state-of-the-art white-box and adaptive adversarial misclassification attacks. We also demonstrate the effectiveness of HRS in defending adversarial reprogramming, which is the first defense against adversarial programs. Moreover, in most settings the average DES of HRS is at least 5X higher than current stochastic network defenses, validating its significantly improved robustness-accuracy trade-off.

Connected through hyperedge HE-1 (induced by source paper "rtmobile beyond real time mobile acceleration of rnns for speech recognition"):
  with members:
    - "deepsz a novel framework to compress deep neural networks by using error bounded lossy compression"
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "systematic weight pruning of dnns using alternating direction method of multipliers"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "advms a multi source multi cost defense against adversarial attacks"):
  with members:
    - "adversarial t shirt evading person detectors in a physical world"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "breaking transferability of adversarial samples with randomness"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "mtdeep boosting the security of deep neural nets against adversarial attacks with moving target defense"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers"):
  with members:
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "adversarial training for free"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "hardening deep neural networks via adversarial model cascades"

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.LG(Machine Learning)