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: "explaining black box android malware detection"
  Abstract: Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions. However, recent findings have highlighted the fragility of such in-vitro evaluations with benchmark datasets, showing that very few changes to the content of Android malware may suffice to evade detection. How can we thus trust that a malware detector performing well on benchmark data will continue to do so when deployed in an operating environment? To mitigate this issue, the most popular Android malware detectors use linear, explainable machine-learning models to easily identify the most influential features contributing to each decision. In this work, we generalize this approach to any black-box machine- learning model, by leveraging a gradient-based approach to identify the most influential local features. This enables using nonlinear models to potentially increase accuracy without sacrificing interpretability of decisions. Our approach also highlights the global characteristics learned by the model to discriminate between benign and malware applications. Finally, as shown by our empirical analysis on a popular Android malware detection task, it also helps identifying potential vulnerabilities of linear and nonlinear models against adversarial manipulations.

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 "adversarial security attacks and perturbations on machine learning and deep learning methods"):
  with members:
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "designing adversarially resilient classifiers using resilient feature engineering"
    - "security for machine learning based systems attacks and challenges during training and inference"
    - "security matters a survey on adversarial machine learning"
    - "security and privacy issues in deep learning"
    - "deep text classification can be fooled"
    - "mlcapsule guarded offline deployment of machine learning as a service"

Connected through hyperedge HE-3 (induced by source paper "deep neural rejection against adversarial examples"):
  with members:
    - "detecting adversarial examples through nonlinear dimensionality reduction"
    - "selectivenet a deep neural network with an integrated reject option"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "max mahalanobis linear discriminant analysis networks"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "on the effectiveness of system api related information for android ransomware detection"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-6 (induced by source paper "towards adversarial malware detection lessons learned from pdf based attacks"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "model reuse attacks on deep learning systems"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "digital investigation of pdf files unveiling traces of embedded malware"

Connected through hyperedge HE-7 (induced by source paper "tesseract eliminating experimental bias in malware classification across space and time"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "benchmarking crimes an emerging threat in systems security"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "why should i trust you explaining the predictions of any classifier"
    - "reviewer integration and performance measurement for malware detection"
    - "learning when training data are costly the effect of class distribution on tree induction"

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)