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: "improving function coverage with munch a hybrid fuzzing and directed symbolic execution approach"
  Abstract: Fuzzing and symbolic execution are popular techniques for finding vulnerabilities and generating test-cases for programs. Fuzzing, a blackbox method that mutates seed input values, is generally incapable of generating diverse inputs that exercise all paths in the program. Due to the path-explosion problem and dependence on SMT solvers, symbolic execution may also not achieve high path coverage. A hybrid technique involving fuzzing and symbolic execution may achieve better function coverage than fuzzing or symbolic execution alone. In this paper, we present Munch, an open-source framework implementing two hybrid techniques based on fuzzing and symbolic execution. We empirically show using nine large open-source programs that overall, Munch achieves higher (in-depth) function coverage than symbolic execution or fuzzing alone. Using metrics based on total analyses time and number of queries issued to the SMT solver, we also show that Munch is more efficient at achieving better function coverage.

Connected through hyperedge HE-1 (induced by source paper "hotfuzz discovering algorithmic denial of service vulnerabilities through guided micro fuzzing"):
  with members:
    - "evaluating fuzz testing"
    - "badger complexity analysis with fuzzing and symbolic execution"
    - "discriminating traces with time"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "rise of the hacrs augmenting autonomous cyber reasoning systems with human assistance"
    - "static analysis for regular expression denial of service attacks"

Connected through hyperedge HE-2 (induced by source paper "verifying software vulnerabilities in iot cryptographic protocols"):
  with members:
    - "v fuzz vulnerability oriented evolutionary fuzzing"
    - "on the security of 2 key triple des"
    - "extracting and verifying cryptographic models from c protocol code by symbolic execution"
    - "smt based bounded model checking for embedded ansi c software"

Connected through hyperedge HE-3 (induced by source paper "semantic fuzzing with zest"):
  with members:
    - "smart greybox fuzzing"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "angora efficient fuzzing by principled search"
    - "superion grammar aware greybox fuzzing"
    - "learn fuzz machine learning for input fuzzing"
    - "synthesizing program input grammars"
    - "automatic wsdl guided test case generation for proper testing of web services"

Connected through hyperedge HE-4 (induced by source paper "enhancing dynamic symbolic execution by automatically learning search heuristics"):
  with members:
    - "concolic testing for deep neural networks"
    - "reinforcement learning for automatic test case prioritization and selection in continuous integration"
    - "firmusb vetting usb device firmware using domain informed symbolic execution"
    - "learn fuzz machine learning for input fuzzing"
    - "a survey of symbolic execution techniques"
    - "abstraction driven concolic testing"

Connected through hyperedge HE-5 (induced by source paper "compositional fuzzing aided by targeted symbolic execution"):
  with members:
    - "reviewing klee s sonar search strategy in context of greybox fuzzing"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "angora efficient fuzzing by principled search"
    - "automatically assessing vulnerabilities discovered by compositional analysis"

Connected through hyperedge HE-6 (induced by source paper "enfuzz from ensemble learning to ensemble fuzzing"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "popular ensemble methods an empirical study"
    - "issues in stacked generalization"

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)