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: "leaksemantic identifying abnormal sensitive network transmissions in mobile applications"
  Abstract: Mobile applications (apps) often transmit sensitive data through network with various intentions. Some transmissions are needed to fulfill the app's functionalities. However, transmissions with malicious receivers may lead to privacy leakage and tend to behave stealthily to evade detection. The problem is twofold: how does one unveil sensitive transmissions in mobile apps, and given a sensitive transmission, how does one determine if it is legitimate? #R##N#In this paper, we propose LeakSemantic, a framework that can automatically locate abnormal sensitive network transmissions from mobile apps. LeakSemantic consists of a hybrid program analysis component and a machine learning component. Our program analysis component combines static analysis and dynamic analysis to precisely identify sensitive transmissions. Compared to existing taint analysis approaches, LeakSemantic achieves better accuracy with fewer false positives and is able to collect runtime data such as network traffic for each transmission. Based on features derived from the runtime data, machine learning classifiers are built to further differentiate between the legal and illegal disclosures. Experiments show that LeakSemantic achieves 91% accuracy on 2279 sensitive connections from 1404 apps.

Connected through hyperedge HE-1 (induced by source paper "android malware detection using large scale network representation learning"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-2 (induced by source paper "android malware detection based on factorization machine"):
  with members:
    - "mamadroid detecting android malware by building markov chains of behavioral models extended version"
    - "adam a method for stochastic optimization"
    - "sound and precise malware analysis for android via pushdown reachability and entry point saturation"

Connected through hyperedge HE-3 (induced by source paper "open source android vulnerability detection tools a survey"):
  with members:
    - "horndroid practical and sound static analysis of android applications by smt solving"
    - "i know what leaked in your pocket uncovering privacy leaks on android apps with static taint analysis"

Connected through hyperedge HE-4 (induced by source paper "keeping context in mind automating mobile app access control with user interface inspection"):
  with members:
    - "the feasibility of dynamically granted permissions aligning mobile privacy with user preferences"
    - "flowintent detecting privacy leakage from user intention to network traffic mapping"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "android permissions remystified a field study on contextual integrity"
    - "conxsense automated context classification for context aware access control"
    - "smote synthetic minority over sampling technique"

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)