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 "a systematic review of fuzzing based on machine learning techniques"):
  Abstract: Security vulnerabilities play a vital role in network security system. Fuzzing technology is widely used as a vulnerability discovery technology to reduce damage in advance. However, traditional fuzzing techniques have many challenges, such as how to mutate input seed files, how to increase code coverage, and how to effectively bypass verification. Machine learning technology has been introduced as a new method into fuzzing test to alleviate these challenges. This paper reviews the research progress of using machine learning technology for fuzzing test in recent years, analyzes how machine learning improve the fuzz process and results, and sheds light on future work in fuzzing. Firstly, this paper discusses the reasons why machine learning techniques can be used for fuzzing scenarios and identifies six different stages in which machine learning have been used. Then this paper systematically study the machine learning based fuzzing models from selection of machine learning algorithm, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. Next, this paper assesses the performance of the machine learning models based on the frequently used evaluation metrics. The results of the evaluation prove that machine learning technology has an acceptable capability of categorize predictive for fuzzing. Finally, the comparison on capability of discovering vulnerabilities between traditional fuzzing tools and machine learning based fuzzing tools is analyzed. The results depict that the introduction of machine learning technology can improve the performance of fuzzing. However, there are still some limitations, such as unbalanced training samples and difficult to extract the characteristics related to vulnerabilities.

Includes members:
  Member "capturing the symptoms of malicious code in electronic documents by file s entropy signal combined with machine learning":
    also connected through other hyperedges:
      (none)
  Member "optimizing seed inputs in fuzzing with machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review of machine learning applications in fuzzing")
  Member "v fuzz vulnerability oriented evolutionary fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "verifying software vulnerabilities in iot cryptographic protocols")
  Member "deep reinforcement fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a review of machine learning applications in fuzzing")
  Member "adaptive grey box fuzz testing with thompson sampling":
    also connected through other hyperedges:
      (none)
  Member "fuzzergym a competitive framework for fuzzing and learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient black box assessment of autonomous vehicle safety")
  Member "smartseed smart seed generation for efficient fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neutaint efficient dynamic taint analysis with neural networks")
  Member "recurrent neural networks for fuzz testing web browsers":
    also connected through other hyperedges:
      (none)

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)