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: "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"
  Abstract: The evolution of mobile malware poses a serious threat to smartphone security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data, rendering most recent machine learning-based malware detection tools (such as Drebin, DroidAPIMiner, and MaMaDroid) ineffective. In this paper, we explore the feasibility of constructing crafted malware samples; examine how machine-learning classifiers can be misled under three different threat models; then conclude that injecting carefully crafted data into training data can significantly reduce detection accuracy. To tackle the problem, we propose KuafuDet, a two-phase learning enhancing approach that learns mobile malware by adversarial detection. KuafuDet includes an offline training phase that selects and extracts features from the training set, and an online detection phase that utilizes the classifier trained by the first phase. To further address the adversarial environment, these two phases are intertwined through a self-adaptive learning scheme, wherein an automated camouflage detector is introduced to filter the suspicious false negatives and feed them back into the training phase. We finally show that KuafuDet can significantly reduce false negatives and boost the detection accuracy by at least 15%. Experiments on more than 250,000 mobile applications demonstrate that KuafuDet is scalable and can be highly effective as a standalone system.

Connected through hyperedge HE-1 (induced by source paper "unblind your apps predicting natural language labels for mobile gui components by deep learning"):
  with members:
    - "storydroid automated generation of storyboard for android apps"
    - "attention is all you need"
    - "convolutional image captioning"
    - "deep residual learning for image recognition"
    - "microsoft coco captions data collection and evaluation server"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-2 (induced by source paper "an empirical assessment of security risks of global android banking apps"):
  with members:
    - "a large scale empirical study on industrial fake apps"
    - "storydroid automated generation of storyboard for android apps"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "identifying relevant information cues for vulnerability assessment using cvss"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "large scale analysis of framework specific exceptions in android apps"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (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-4 (induced by source paper "the threat of adversarial attacks on machine learning in network security a survey"):
  with members:
    - "improving network intrusion detection classiﬁers by non payload based exploit independent obfuscations an adversarial approach"
    - "detecting cyberattacks in industrial control systems using convolutional neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "an adaptive system for detecting malicious queries in web attacks"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "exploring adversarial examples in malware detection"
    - "is feature selection secure against training data poisoning"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-5 (induced by source paper "things you may not know about adversarial example a black box adversarial image attack"):
  with members:
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "testing deep neural networks"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-6 (induced by source paper "storydroid automated generation of storyboard for android apps"):
  with members:
    - "a large scale empirical study on industrial fake apps"
    - "understanding android obfuscation techniques a large scale investigation in the wild"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "automated reporting of gui design violations for mobile apps"
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "large scale analysis of framework specific exceptions in android apps"
    - "pix2code generating code from a graphical user interface screenshot"

Connected through hyperedge HE-7 (induced by source paper "a large scale empirical study on industrial fake apps"):
  with members:
    - "storydroid automated generation of storyboard for android apps"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "large scale analysis of framework specific exceptions in android apps"

Connected through hyperedge HE-8 (induced by source paper "adversarial samples on android malware detection systems for iot systems"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"

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)