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: "differential testing for variational analyses experience from developing kconfigreader"
  Abstract: Differential testing to solve the oracle problem has been applied in many scenarios where multiple supposedly equivalent implementations exist, such as multiple implementations of a C compiler. If the multiple systems disagree on the output for a given test input, we have likely discovered a bug without every having to specify what the expected output is. Research on variational analyses (or variability-aware or family-based analyses) can benefit from similar ideas. The goal of most variational analyses is to perform an analysis, such as type checking or model checking, over a large number of configurations much faster than an existing traditional analysis could by analyzing each configuration separately. Variational analyses are very suitable for differential testing, since the existence nonvariational analysis can provide the oracle for test cases that would otherwise be tedious or difficult to write. In this experience paper, I report how differential testing has helped in developing KConfigReader, a tool for translating the Linux kernel's kconfig model into a propositional formula. Differential testing allows us to quickly build a large test base and incorporate external tests that avoided many regressions during development and made KConfigReader likely the most precise kconfig extraction tool available.

Connected through hyperedge HE-1 (induced by source paper "causal testing finding defects root causes"):
  with members:
    - "automated test generation to detect individual discrimination in ai models"
    - "fairness testing testing software for discrimination"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "spectrum based software fault localization a survey of techniques advances and challenges"

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)