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: "n gram opcode analysis for android malware detection"
  Abstract: Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends.

Connected through hyperedge HE-1 (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"
    - "android malware detection using deep learning on api method sequences"
    - "emulator vs real phone android malware detection using machine learning"
    - "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-2 (induced by source paper "towards safer smart contracts a sequence learning approach to detecting security threats"):
  with members:
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "tiresias predicting security events through deep learning"
    - "securify practical security analysis of smart contracts"
    - "attention is all you need"
    - "an empirical analysis of smart contracts platforms applications and design patterns"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

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)