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: "android malware detection using deep learning on api method sequences"
  Abstract: Android OS experiences a blazing popularity since the last few years. This predominant platform has established itself not only in the mobile world but also in the Internet of Things (IoT) devices. This popularity, however, comes at the expense of security, as it has become a tempting target of malicious apps. Hence, there is an increasing need for sophisticated, automatic, and portable malware detection solutions. In this paper, we propose MalDozer, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques. Starting from the raw sequence of the app's API method calls, MalDozer automatically extracts and learns the malicious and the benign patterns from the actual samples to detect Android malware. MalDozer can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices. We evaluate MalDozer on multiple Android malware datasets ranging from 1K to 33K malware apps, and 38K benign apps. The results show that MalDozer can correctly detect malware and attribute them to their actual families with an F1-Score of 96%-99% and a false positive rate of 0.06%-2%, under all tested datasets and settings.

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 "dl droid deep learning based android malware detection using real devices"):
  with members:
    - "improving dynamic analysis of android apps using hybrid test input generation"
    - "emulator vs real phone android malware detection using machine learning"
    - "n gram opcode analysis for android malware detection"
    - "n opcode analysis for android malware classification and categorization"
    - "dynalog an automated dynamic analysis framework for characterizing android applications"
    - "android malware detection an eigenspace analysis approach"
    - "automated test input generation for android are we there yet"
    - "high accuracy android malware detection using ensemble learning"

Connected through hyperedge HE-3 (induced by source paper "rnnsecurenet recurrent neural networks for cyber security use cases"):
  with members:
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "portable data driven malware detection using language processing and machine learning techniques on behavioral analysis reports"):
  with members:
    - "automatic investigation framework for android malware cyber infrastructures"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "dynalog an automated dynamic analysis framework for characterizing android applications"

Connected through hyperedge HE-5 (induced by source paper "deep net deep neural network for cyber security use cases"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)