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: "evaluating fuzz testing"
  Abstract: Fuzz testing has enjoyed great success at discovering security critical bugs in real software. Recently, researchers have devoted significant effort to devising new fuzzing techniques, strategies, and algorithms. Such new ideas are primarily evaluated experimentally so an important question is: What experimental setup is needed to produce trustworthy results? We surveyed the recent research literature and assessed the experimental evaluations carried out by 32 fuzzing papers. We found problems in every evaluation we considered. We then performed our own extensive experimental evaluation using an existing fuzzer. Our results showed that the general problems we found in existing experimental evaluations can indeed translate to actual wrong or misleading assessments. We conclude with some guidelines that we hope will help improve experimental evaluations of fuzz testing algorithms, making reported results more robust.

Connected through hyperedge HE-1 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing"):
  with members:
    - "a review of machine learning applications in fuzzing"
    - "ptrix efficient hardware assisted fuzzing for cots binary"
    - "savior towards bug driven hybrid testing"
    - "learning a sat solver from single bit supervision"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "angora efficient fuzzing by principled search"

Connected through hyperedge HE-2 (induced by source paper "binary level directed fuzzing for use after free vulnerabilities"):
  with members:
    - "matryoshka fuzzing deeply nested branches"
    - "weizz automatic grey box fuzzing for structured binary formats"
    - "get rid of inline assembly through verification oriented lifting"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "assurances in software testing a roadmap"
    - "sok sanitizing for security"
    - "the art science and engineering of fuzzing a survey"
    - "neuzz efficient fuzzing with neural program smoothing"

Connected through hyperedge HE-3 (induced by source paper "hotfuzz discovering algorithmic denial of service vulnerabilities through guided micro fuzzing"):
  with members:
    - "improving function coverage with munch a hybrid fuzzing and directed symbolic execution approach"
    - "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-4 (induced by source paper "montage a neural network language model guided javascript engine fuzzer"):
  with members:
    - "learn fuzz machine learning for input fuzzing"
    - "exploring the limits of language modeling"
    - "character aware neural language models"
    - "structured generative models of natural source code"
    - "a bit of progress in language modeling"

Connected through hyperedge HE-5 (induced by source paper "a review of machine learning applications in fuzzing"):
  with members:
    - "optimizing seed inputs in fuzzing with machine learning"
    - "deep reinforcement fuzzing"
    - "learning a sat solver from single bit supervision"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "angora efficient fuzzing by principled search"
    - "automatic heap layout manipulation for exploitation"
    - "graph neural networks and boolean satisfiability"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-6 (induced by source paper "targeted greybox fuzzing with static lookahead analysis"):
  with members:
    - "v fuzz vulnerability oriented evolutionary fuzzing"
    - "harvey a greybox fuzzer for smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "online detection of effectively callback free objects with applications to smart contracts"
    - "quantitative analysis of smart contracts"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "learning inputs in greybox fuzzing"

Connected through hyperedge HE-7 (induced by source paper "oracle supported dynamic exploit generation for smart contracts"):
  with members:
    - "precise attack synthesis for smart contracts"
    - "exploiting the laws of order in smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "sereum protecting existing smart contracts against re entrancy attacks"
    - "quantitative analysis of smart contracts"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "learning inputs in greybox fuzzing"
    - "towards safer smart contracts a survey of languages and verification methods"

Connected through hyperedge HE-8 (induced by source paper "harvey a greybox fuzzer for smart contracts"):
  with members:
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "online detection of effectively callback free objects with applications to smart contracts"
    - "quantitative analysis of smart contracts"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "vandal a scalable security analysis framework for smart contracts"
    - "angora efficient fuzzing by principled search"
    - "securify practical security analysis of smart contracts"
    - "a concurrent perspective on smart contracts"

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.CR(Cryptography and Security)