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 "ransomware analysis using feature engineering and deep neural networks"):
  Abstract: Detection and Analysis of a potential malware specifically, used for ransom is a challenging task. Recently, intruders are utilizing advance cryptographic techniques to get hold of digital assets and then demand ransom. It is believed that generally, the files comprise of some attributes, states, and patterns that can be recognized by a machine learning technique. This work thus focuses on detection of Ransomware by performing feature engineering, which helps in analyzing vital attributes and behaviors of the malware. The main contribution of this work is the identification of important and distinct characteristics of Ransomware that can help in detecting them. Finally, based on the selected features, both conventional machine learning techniques and Transfer Learning based Deep Convolutional Neural Networks have been used to detect Ransomware. In order to perform feature engineering and analysis, two separate datasets (static and dynamic) were generated. The static dataset has 3646 samples (1700 Ransomware and 1946 Goodware). On the other hand, the dynamic dataset comprised of 3444 samples (1455 Ransomware and 1989 Goodware). Through various experiments, it is observed that the Registry changes, API calls, and DLLs are the most important features for Ransomware detection. Additionally, important sequences are found with the help of N Gram technique. It is also observed that in case of Registry Delete operation, if a malicious file tries to delete registries, it follows a specific and repeated sequence. However for the benign file, it doesnt follow any specific sequence or repetition. Similarly, an interesting observation made through this study is that there is no common Registry deleted sequence between malicious and benign file. And thus this discernible fact can be readily exploited for Ransomware detection. The relevant Python code and dataset are available at github.

Includes members:
  Member "a recent survey on the applications of genetic programming in image processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transfer learning and meta classification based deep churn prediction system for telecom industry")
  Member "a survey of the recent architectures of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a precisely xtreme multi channel hybrid approach for roman urdu sentiment analysis")
  Member "a new channel boosted convolution neural network using transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer learning and meta classification based deep churn prediction system for telecom industry")
  Member "software defined networking based crypto ransomware detection using http traffic characteristics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "volenti non fit injuria ransomware and its victims")
  Member "automated behavioral analysis of malware a case study of wannacry ransomware":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automated ransomware behavior analysis pattern extraction and early detection")
  Member "automated dynamic analysis of ransomware benefits limitations and use for detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards a resilient machine learning classifier a case study of ransomware detection")
  Member "deep neural network based malware detection using two dimensional binary program features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)