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: "mamadroid detecting android malware by building markov chains of behavioral models"
  Abstract: The rise in popularity of the Android platform has resulted in an explosion of malware threats targeting it. As both Android malware and the operating system itself constantly evolve, it is very challenging to design robust malware mitigation techniques that can operate for long periods of time without the need for modifications or costly re-training. In this paper, we present MaMaDroid, an Android malware detection system that relies on app behavior. MaMaDroid builds a behavioral model, in the form of a Markov chain, from the sequence of abstracted API calls performed by an app, and uses it to extract features and perform classification. By abstracting calls to their packages or families, MaMaDroid maintains resilience to API changes and keeps the feature set size manageable. We evaluate its accuracy on a dataset of 8.5K benign and 35.5K malicious apps collected over a period of six years, showing that it not only effectively detects malware (with up to 99% F-measure), but also that the model built by the system keeps its detection capabilities for long periods of time (on average, 86% and 75% F-measure, respectively, one and two years after training). Finally, we compare against DroidAPIMiner, a state-of-the-art system that relies on the frequency of API calls performed by apps, showing that MaMaDroid significantly outperforms it.

Connected through hyperedge HE-1 (induced by source paper "hidden in plain sight obfuscated strings threatening your privacy"):
  with members:
    - "understanding android obfuscation techniques a large scale investigation in the wild"
    - "a large scale investigation of obfuscation use in google play"
    - "an investigation into the use of common libraries in android apps"

Connected through hyperedge HE-2 (induced by source paper "new era of deeplearning based malware intrusion detection the malware detection and prediction based on deep learning"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "improved training of wasserstein gans"
    - "long text generation via adversarial training with leaked information"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "xgboost a scalable tree boosting system"
    - "very deep convolutional networks for text classification"
    - "n opcode analysis for android malware classification and categorization"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-3 (induced by source paper "scalable online vetting of android apps for measuring declared sdk versions and their consistency with api calls"):
  with members:
    - "sclib a practical and lightweight defense against component hijacking in android applications"
    - "discovering flaws in security focused static analysis tools for android using systematic mutation"
    - "mopeye opportunistic monitoring of per app mobile network performance"
    - "measuring the declared sdk versions and their consistency with api calls in android apps"
    - "indirect file leaks in mobile applications"
    - "analyzing android browser apps for file vulnerabilities"
    - "a sink driven approach to detecting exposed component vulnerabilities in android apps"

Connected through hyperedge HE-4 (induced by source paper "don t pick the cherry an evaluation methodology for android malware detection methods"):
  with members:
    - "android hiv a study of repackaging malware for evading machine learning detection"
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "rebooting research on detecting repackaged android apps literature review and benchmark"
    - "yes machine learning can be more secure a case study on android malware detection"

Connected through hyperedge HE-5 (induced by source paper "diffusing your mobile apps extending in network function virtualization to mobile function offloading"):
  with members:
    - "automated test input generation for android are we there yet"

Connected through hyperedge HE-6 (induced by source paper "dadidroid an obfuscation resilient tool for detecting android malware via weighted directed call graph modelling"):
  with members:
    - "understanding android obfuscation techniques a large scale investigation in the wild"

Connected through hyperedge HE-7 (induced by source paper "android hiv a study of repackaging malware for evading machine learning detection"):
  with members:
    - "yes machine learning can be more secure a case study on android malware detection"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "synthesizing robust adversarial examples"
    - "adversarial examples for evaluating reading comprehension systems"
    - "adversarial examples that fool detectors"
    - "adversarial examples attacks and defenses for deep learning"

Connected through hyperedge HE-8 (induced by source paper "malytics a malware detection scheme"):
  with members:
    - "attack and defense of dynamic analysis based adversarial neural malware classification models"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "dynamic routing between capsules"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "fast memory efficient anomaly detection in streaming heterogeneous graphs"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "towards evaluating the robustness of neural 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)