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: "adaptive and scalable android malware detection through online learning"
  Abstract: It is well-known that malware constantly evolves so as to evade detection and this causes the entire malware population to be non-stationary. Contrary to this fact, prior works on machine learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i.e., features) do not change over time. In this work, we address the problem of malware population drift and propose a novel online machine learning based framework, named DroidOL to handle it and effectively detect malware. In order to perform accurate detection, security-sensitive behaviors are captured from apps in the form of inter-procedural control-flow sub-graph features using a state-of-the-art graph kernel. In order to perform scalable detection and to adapt to the drift and evolution in malware population, an online passive-aggressive classifier is used. #R##N#In a large-scale comparative analysis with more than 87,000 apps, DroidOL achieves 84.29% accuracy outperforming two state-of-the-art malware techniques by more than 20% in their typical batch learning setting and more than 3% when they are continuously re-trained. Our experimental findings strongly indicate that online learning based approaches are highly suitable for real-world malware detection.

Connected through hyperedge HE-1 (induced by source paper "a survey on the detection of android malicious apps"):
  with members:
    - "group wise classification approach to improve android malicious apps detection accuracy"
    - "an investigation of the classifiers to detect android malicious apps"
    - "an effective approach for classification of advanced malware with high accuracy"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"
    - "n opcode analysis for android malware classification and categorization"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"
    - "droidanalytics a signature based analytic system to collect extract analyze and associate android malware"

Connected through hyperedge HE-2 (induced by source paper "group wise classification approach to improve android malicious apps detection accuracy"):
  with members:
    - "an investigation of the classifiers to detect android malicious apps"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

Connected through hyperedge HE-3 (induced by source paper "the curious case of machine learning in malware detection"):
  with members:
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "developing a k ary malware using blockchain"
    - "deepsign deep learning for automatic malware signature generation and classification"

Connected through hyperedge HE-4 (induced by source paper "a multi view context aware approach to android malware detection and malicious code localization"):
  with members:
    - "poisoning behavioral malware clustering"
    - "a unifying view of explicit and implicit feature maps for structured data systematic studies of graph kernels"
    - "contextual weisfeiler lehman graph kernel for malware detection"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "why should i trust you explaining the predictions of any classifier"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"

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)