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: "software reliability growth models predict autonomous vehicle disengagement events"
  Abstract: The acceptance of autonomous vehicles is dependent on the rigorous assessment of their safety. Furthermore, the commercial viability of AV programs depends on the ability to estimate the time and resources required to achieve desired safety levels. Naive approaches to estimating the reliability and safety levels of autonomous vehicles under development are will require infeasible amounts of testing of a static vehicle configuration. To permit both the estimation of current safety, and make predictions about the reliability of future systems, I propose the use of a standard tool for modelling the reliability of evolving software systems, software reliability growth models (SRGMs). #R##N#Publicly available data from Californian public-road testing of two autonomous vehicle systems is modelled using two of the best-known SRGMs. The ability of the models to accurately estimate current reliability, as well as for current testing data to predict reliability in the future after additional testing, is evaluated. One of the models, the Musa-Okumoto model, appears to be a good estimator and a reasonable predictor.

Connected through hyperedge HE-1 (induced by source paper "cagfuzz coverage guided adversarial generative fuzzing testing of deep learning systems"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "semantic fuzzing with zest"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "deephunter hunting deep neural network defects via coverage guided fuzzing"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "testing deep neural networks"
    - "superion grammar aware greybox fuzzing"

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.SE(Software Engineering)