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 "meuzz smart seed scheduling for hybrid fuzzing"):
  Abstract: Seed scheduling is a prominent factor in determining the yields of hybrid fuzzing. Existing hybrid fuzzers schedule seeds based on fixed heuristics that aim to predict input utilities. However, such heuristics are not generalizable as there exists no one-size-fits-all rule applicable to different programs. They may work well on the programs from which they were derived, but not others. #R##N#To overcome this problem, we design a Machine learning-Enhanced hybrid fUZZing system (MEUZZ), which employs supervised machine learning for adaptive and generalizable seed scheduling. MEUZZ determines which new seeds are expected to produce better fuzzing yields based on the knowledge learned from past seed scheduling decisions made on the same or similar programs. MEUZZ's learning is based on a series of features extracted via code reachability and dynamic analysis, which incurs negligible runtime overhead (in microseconds). Moreover, MEUZZ automatically infers the data labels by evaluating the fuzzing performance of each selected seed. As a result, MEUZZ is generally applicable to, and performs well on, various kinds of programs. #R##N#Our evaluation shows MEUZZ significantly outperforms the state-of-the-art grey-box and hybrid fuzzers, achieving 27.1% more code coverage than QSYM. The learned models are reusable and transferable, which boosts fuzzing performance by 7.1% on average and improves 68% of the 56 cross-program fuzzing campaigns. MEUZZ discovered 47 deeply hidden and previously unknown bugs--with 21 confirmed and fixed by the developers--when fuzzing 8 well-tested programs with the same configurations as used in previous work.

Includes members:
  Member "a review of machine learning applications in fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "empirical notes on the interaction between continuous kernel fuzzing and development")
  Member "ptrix efficient hardware assisted fuzzing for cots binary":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "savior towards bug driven hybrid testing")
  Member "savior towards bug driven hybrid testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "specusym speculative symbolic execution for cache timing leak detection")
  Member "learning a sat solver from single bit supervision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "teaching temporal logics to neural networks")
  Member "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "binary level directed fuzzing for use after free vulnerabilities")
  Member "evaluating fuzz testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "binary level directed fuzzing for use after free vulnerabilities")
  Member "vuldeepecker a deep learning based system for vulnerability detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "what you see is what it means semantic representation learning of code based on visualization and transfer learning")
  Member "neuzz efficient fuzzing with neural program smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacy for all demystify vulnerability disparity of differential privacy against membership inference attack")

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)