human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mamadroid detecting android malware by building markov chains of behavioral models extended version"):
  Abstract: As Android becomes increasingly popular, so does malware targeting it, this motivating the research community to propose many different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes it hard to design robust tools that can operate for long periods of time without the need for modifications or costly re-training. Aiming to address this issue, we set to detect malware from a behavioral point of view, modeled as the sequence of abstracted API calls. We introduce MaMaDroid, a static-analysis based system that abstracts app's API calls to their class, package, or family, and builds a model from their sequences obtained from the call graph of an app as Markov chains. This ensures that the model is more resilient to API changes and the features set is of manageable size. We evaluate MaMaDroid using a dataset of 8.5K benign and 35.5K malicious apps collected over a period of six years, showing that it effectively detects malware (with up to 0.99 F-measure) and keeps its detection capabilities for long periods of time (up to 0.87 F-measure two years after training). We also show that MaMaDroid remarkably improves over DroidAPIMiner, a state-of-the-art detection system that relies on the frequency of (raw) API calls. Aiming to assess whether MaMaDroid's effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls. We find that it is not as accurate, failing to capture maliciousness when trained on malware samples including API calls that are equally or more frequently used by benign apps.

Includes members:
  Member "tesseract eliminating experimental bias in malware classification across space and time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on model evaluation under non constant class imbalance")
  Member "a family of droids android malware detection via behavioral modeling static vs dynamic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integration of static and dynamic analysis for malware family classification with composite neural network")
  Member "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unblind your apps predicting natural language labels for mobile gui components by deep learning")
  Member "mamadroid detecting android malware by building markov chains of behavioral models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hidden in plain sight obfuscated strings threatening your privacy")
  Member "automated synthesis of semantic malware signatures using maximum satisfiability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "counterexample guided neural synthesis")

Question: Please tell me which class the source paper 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)