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: "automated dynamic analysis of ransomware benefits limitations and use for detection"
  Abstract: Recent statistics show that in 2015 more than 140 millions new malware samples have been found. Among these, a large portion is due to ransomware, the class of malware whose specific goal is to render the victim's system unusable, in particular by encrypting important files, and then ask the user to pay a ransom to revert the damage. Several ransomware include sophisticated packing techniques, and are hence difficult to statically analyse. We present EldeRan, a machine learning approach for dynamically analysing and classifying ransomware. EldeRan monitors a set of actions performed by applications in their first phases of installation checking for characteristics signs of ransomware. Our tests over a dataset of 582 ransomware belonging to 11 families, and with 942 goodware applications, show that EldeRan achieves an area under the ROC curve of 0.995. Furthermore, EldeRan works without requiring that an entire ransomware family is available beforehand. These results suggest that dynamic analysis can support ransomware detection, since ransomware samples exhibit a set of characteristic features at run-time that are common across families, and that helps the early detection of new variants. We also outline some limitations of dynamic analysis for ransomware and propose possible solutions.

Connected through hyperedge HE-1 (induced by source paper "towards a resilient machine learning classifier a case study of ransomware detection"):
  with members:
    - "an introduction to variational autoencoders"
    - "towards resilient machine learning for ransomware detection"
    - "sanity checks for saliency maps"
    - "axiomatic attribution for deep networks"
    - "understanding data augmentation for classification when to warp"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "a kings ransom for encryption ransomware classification using augmented one shot learning and bayesian approximation"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "temporal graph offset reconstruction towards temporally robust graph representation learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bayesian layers a module for neural network uncertainty"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "one shot learning with memory augmented neural networks"

Connected through hyperedge HE-3 (induced by source paper "neurlux dynamic malware analysis without feature engineering"):
  with members:
    - "ember an open dataset for training static pe malware machine learning models"
    - "early stage malware prediction using recurrent neural networks"
    - "attention is all you need"
    - "learning transferable features with deep adaptation networks"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "a c lstm neural network for text classification"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "volenti non fit injuria ransomware and its victims"):
  with members:
    - "temporal graph offset reconstruction towards temporally robust graph representation learning"
    - "software defined networking based crypto ransomware detection using http traffic characteristics"
    - "on the economics of ransomware"
    - "exploit kits the production line of the cybercrime economy"
    - "node2vec scalable feature learning for networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "ransomware analysis using feature engineering and deep neural networks"):
  with members:
    - "a recent survey on the applications of genetic programming in image processing"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "a new channel boosted convolution neural network using transfer learning"
    - "software defined networking based crypto ransomware detection using http traffic characteristics"
    - "automated behavioral analysis of malware a case study of wannacry ransomware"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "towards resilient machine learning for ransomware detection"):
  with members:
    - "deep learning for malicious flow detection"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting adversarial examples via neural fingerprinting"
    - "leveraging machine learning techniques for windows ransomware network traffic detection"
    - "adversarial logit pairing"
    - "deep transfer learning for static malware classification"
    - "generating adversarial examples with adversarial networks"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-7 (induced by source paper "leveraging machine learning techniques for windows ransomware network traffic detection"):
  with members:
    - "greening cloud enabled big data storage forensics syncany as a case study"
    - "cloudme forensics a case of big data forensic investigation"
    - "a cyber kill chain based taxonomy of banking trojans for evolutionary computational intelligence"
    - "know abnormal find evil frequent pattern mining for ransomware threat hunting and intelligence"
    - "exploit kits the production line of the cybercrime economy"
    - "automated detection and classification of cryptographic algorithms in binary programs through machine learning"

Connected through hyperedge HE-8 (induced by source paper "portable data driven malware detection using language processing and machine learning techniques on behavioral analysis reports"):
  with members:
    - "automatic investigation framework for android malware cyber infrastructures"
    - "android malware detection using deep learning on api method sequences"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "dynalog an automated dynamic analysis framework for characterizing android applications"

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)