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: "evading classifiers by morphing in the dark"
  Abstract: Learning-based systems have been shown to be vulnerable to evasion through adversarial data manipulation. These attacks have been studied under assumptions that the adversary has certain knowledge of either the target model internals, its training dataset or at least classification scores it assigns to input samples. In this paper, we investigate a much more constrained and realistic attack scenario wherein the target classifier is minimally exposed to the adversary, revealing only its final classification decision (e.g., reject or accept an input sample). Moreover, the adversary can only manipulate malicious samples using a blackbox morpher. That is, the adversary has to evade the targeted classifier by morphing malicious samples "in the dark". We present a scoring mechanism that can assign a real-value score which reflects evasion progress to each sample based on the limited information available. Leveraging on such scoring mechanism, we propose an evasion method -- EvadeHC? and evaluate it against two PDF malware detectors, namely PDFRate and Hidost. The experimental evaluation demonstrates that the proposed evasion attacks are effective, attaining 100% evasion rate on the evaluation dataset. Interestingly, EvadeHC outperforms the known classifier evasion techniques that operate based on classification scores output by the classifiers. Although our evaluations are conducted on PDF malware classifiers, the proposed approaches are domain agnostic and are of wider application to other learning-based systems.

Connected through hyperedge HE-1 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers"):
  with members:
    - "understanding the efficacy reliability and resiliency of computer vision techniques for malware detection and future research directions"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "on defending against label flipping attacks on malware detection systems"
    - "adversarial samples on android malware detection systems for iot systems"
    - "copycat practical adversarial attacks on visualization based malware detection"
    - "generation evaluation of adversarial examples for malware obfuscation"
    - "examining adversarial learning against graph based iot malware detection systems"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"

Connected through hyperedge HE-2 (induced by source paper "optimization guided binary diversification to mislead neural networks for malware detection"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "theoretically principled trade off between robustness and accuracy"
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "ember an open dataset for training static pe malware machine learning models"
    - "feature denoising for improving adversarial robustness"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "on designing machine learning models for malicious network traffic classification"
    - "rallying adversarial techniques against deep learning for network security"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deceiving end to end deep learning malware detectors using adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "on training robust pdf malware classifiers"):
  with members:
    - "robust decision trees against adversarial examples"
    - "enhancing gradient based attacks with symbolic intervals"
    - "scaling provable adversarial defenses"
    - "adversarial attack on graph structured data"
    - "formal security analysis of neural networks using symbolic intervals"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "cost sensitive robustness against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "one pixel attack for fooling deep neural networks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "self organizing classifiers first steps in structured evolutionary machine learning"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "self organizing classifiers and niched fitness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "classification regions of deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "effectiveness of distillation attack and countermeasure on neural network watermarking"):
  with members:
    - "deep learning is robust to massive label noise"
    - "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"
    - "are you tampering with my data"
    - "embedding watermarks into deep neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adversarial frontier stitching for remote neural network watermarking"

Connected through hyperedge HE-7 (induced by source paper "a formalization of robustness for deep neural networks"):
  with members:
    - "counterexample guided data augmentation"
    - "semantic adversarial deep learning"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "systematic testing of convolutional neural networks for autonomous driving"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "intriguing properties of adversarial ml attacks in the problem space"):
  with members:
    - "misleading authorship attribution of source code using adversarial learning"
    - "on evaluating adversarial robustness"
    - "tesseract eliminating experimental bias in malware classification across space and time"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "textbugger generating adversarial text against real world applications"
    - "generating natural language adversarial examples"
    - "adversarial malware binaries evading deep learning for malware detection in executables"

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)