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: "dynamic data fusion using multi input models for malware classification"
  Abstract: Criminals use malware to disrupt cyber-systems. The number of these malware-vulnerable systems is increasing quickly as common systems, such as vehicles, routers, and lightbulbs, become increasingly interconnected cyber-systems. To address the scale of this problem, analysts divide malware into classes and develop, for each class, a specialized defense. In this project we classified malware with machine learning. In particular, we used a supervised multi-class long short term memory (LSTM) model. We trained the algorithm with thousands of malware files annotated with class labels (the training set), and the algorithm learned patterns indicative of each class. We used disassembled malware files (provided by Microsoft) and separated the constituent data into parsed instructions, which look like human-readable machine code text, and raw bytes, which are hexadecimal values. We are interested in which format, text or hex, is more valuable as input for classification. To solve this, we investigated four cases: a text-only model, a hexadecimal-only model, a multi-input model using both text and hexadecimal inputs, and a model based on combining the individual results. We performed this investigation using the machine learning Python package Keras, which allows easily configurable deep learning architectures and training. We hoped to understand the trade-offs between the different formats. Due to the class imbalance in the data, we used multiple methods to compare the formats, using test accuracies, balanced accuracies (taking into account weights of classes), and an accuracy derived from tables of confusion. We found that the multi-input model, which allows learning on both input types simultaneously, resulted in the best performance. Our finding expedites malware classification research by providing researchers a suitable deep learning architecture to train a tailored version to their malware.

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)