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: "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
  Abstract: In recent years, fuzz testing has proven itself to be one of the most effective techniques for finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing tool, American Fuzzy Lop (AFL), has become popular thanks to its ease-of-use and bug-finding power. However, AFL remains limited in the bugs it can find since it simply does not cover large regions of code. If it does not cover parts of the code, it will not find bugs there. We propose a two-pronged approach to increase the coverage achieved by AFL. First, the approach automatically identifies branches exercised by few AFL-produced inputs (rare branches), which often guard code that is empirically hard to cover by naively mutating inputs. The second part of the approach is a novel mutation mask creation algorithm, which allows mutations to be biased towards producing inputs hitting a given rare branch. This mask is dynamically computed during fuzz testing and can be adapted to other testing targets. We implement this approach on top of AFL in a tool named FairFuzz. We conduct evaluation on real-world programs against state-of-the-art versions of AFL. We find that on these programs FairFuzz achieves high branch coverage at a faster rate that state-of-the-art versions of AFL. In addition, on programs with nested conditional structure, it achieves sustained increases in branch coverage after 24 hours (average 10.6% increase). In qualitative analysis, we find that FairFuzz has an increased capacity to automatically discover keywords.

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"
    - "evaluating fuzz testing"
    - "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"
    - "evaluating fuzz testing"
    - "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 "learning transferable graph exploration"):
  with members:
    - "synthetic datasets for neural program synthesis"
    - "dom q net grounded rl on structured language"
    - "learning a sat solver from single bit supervision"
    - "learning to search with mctsnets"
    - "meta reinforcement learning of structured exploration strategies"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "diversity is all you need learning skills without a reward function"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-4 (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"
    - "evaluating fuzz testing"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "learning inputs in greybox fuzzing"

Connected through hyperedge HE-5 (induced by source paper "synfuzz efficient concolic execution via branch condition synthesis"):
  with members:
    - "evaluating fuzz testing"
    - "angora efficient fuzzing by principled search"
    - "program synthesis using conflict driven learning"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "learn fuzz machine learning for input fuzzing"
    - "deepcoder learning to write programs"
    - "synthesizing program input grammars"

Connected through hyperedge HE-6 (induced by source paper "semantic fuzzing with zest"):
  with members:
    - "smart greybox fuzzing"
    - "evaluating fuzz testing"
    - "improving function coverage with munch a hybrid fuzzing and directed symbolic execution approach"
    - "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-7 (induced by source paper "evmfuzz differential fuzz testing of ethereum virtual machine"):
  with members:
    - "quanfuzz fuzz testing of quantum program"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "enfuzz from ensemble learning to ensemble fuzzing"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-8 (induced by source paper "matryoshka fuzzing deeply nested branches"):
  with members:
    - "deep reinforcement fuzzing"
    - "angora efficient fuzzing by principled search"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "learn fuzz machine learning for input 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)