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: "stads software testing as species discovery"
  Abstract: A fundamental challenge of software testing is the statistically well-grounded extrapolation from program behaviors observed during testing. For instance, a security researcher who has run the fuzzer for a week has currently no means (i) to estimate the total number of feasible program branches, given that only a fraction has been covered so far, (ii) to estimate the additional time required to cover 10% more branches, or (iii) to assess the residual risk that a vulnerability exists when no vulnerability has been discovered. Failing to discover a vulnerability, does not mean that none exists---even if the fuzzer was run for a week (or a year). Hence, testing provides no formal correctness guarantees. #R##N#In this article, I establish an unexpected connection with the otherwise unrelated scientific field of ecology, and introduce a statistical framework that models Software Testing and Analysis as Discovery of Species (STADS). For instance, in order to study the species diversity of arthropods in a tropical rain forest, ecologists would first sample a large number of individuals from that forest, determine their species, and extrapolate from the properties observed in the sample to properties of the whole forest. The estimation (i) of the total number of species, (ii) of the additional sampling effort required to discover 10% more species, or (iii) of the probability to discover a new species are classical problems in ecology. The STADS framework draws from over three decades of research in ecological biostatistics to address the fundamental extrapolation challenge for automated test generation. Our preliminary empirical study demonstrates a good estimator performance even for a fuzzer with adaptive sampling bias---AFL, a state-of-the-art vulnerability detection tool. The STADS framework provides statistical correctness guarantees with quantifiable accuracy.

Connected through hyperedge HE-1 (induced by source paper "human in the loop automatic program repair"):
  with members:
    - "mcpa program analysis as machine learning"
    - "active learning for binary classification with abstention"
    - "automatic software repair a bibliography"

Connected through hyperedge HE-2 (induced by source paper "the art science and engineering of fuzzing a survey"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "angora efficient fuzzing by principled search"
    - "automated detection exploitation and elimination of double fetch bugs using modern cpu features"
    - "learn fuzz machine learning for input fuzzing"
    - "synthesizing program input grammars"

Connected through hyperedge HE-3 (induced by source paper "diffuzz differential fuzzing for side channel analysis"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "badger complexity analysis with fuzzing and symbolic execution"
    - "stacco differentially analyzing side channel traces for detecting ssl tls vulnerabilities in secure enclaves"

Connected through hyperedge HE-4 (induced by source paper "assurances in software testing a roadmap"):
  with members:
    - "strong consistency of the good turing estimator"

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)