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 "contaminant removal for android malware detection systems"):
  Abstract: A recent report indicates that there is a new malicious app introduced every 4 seconds. This rapid malware distribution rate causes existing malware detection systems to fall far behind, allowing malicious apps to escape vetting efforts and be distributed by even legitimate app stores. When trusted downloading sites distribute malware, several negative consequences ensue. First, the popularity of these sites would allow such malicious apps to quickly and widely infect devices. Second, analysts and researchers who rely on machine learning based detection techniques may also download these apps and mistakenly label them as benign since they have not been disclosed as malware. These apps are then used as part of their benign dataset during model training and testing. The presence of contaminants in benign dataset can compromise the effectiveness and accuracy of their detection and classification techniques. To address this issue, we introduce PUDROID (Positive and Unlabeled learning-based malware detection for Android) to automatically and effectively remove contaminants from training datasets, allowing machine learning based malware classifiers and detectors to be more effective and accurate. To further improve the performance of such detectors, we apply a feature selection strategy to select pertinent features from a variety of features. We then compare the detection rates and accuracy of detection systems using two datasets; one using PUDROID to remove contaminants and the other without removing contaminants. The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy

Includes members:
  Member "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a convolutional neural network for user identification based on motion sensors")
  Member "multi view unsupervised feature selection by cross diffused matrix alignment":
    also connected through other hyperedges:
      (none)
  Member "joint deep modeling of users and items using reviews for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient structure preserving support tensor train machine")

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)