human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "synfuzz efficient concolic execution via branch condition synthesis"):
  Abstract: Concolic execution is a powerful program analysis technique for exploring execution paths in a systematic manner. Compare to random-mutation-based fuzzing, concolic execution is especially good at exploring paths that are guarded by complex and tight branch predicates (e.g., (a*b) == 0xdeadbeef). The drawback, however, is that concolic execution engines are much slower than native execution. One major source of the slowness is that concolic execution engines have to the interpret instructions to maintain the symbolic expression of program variables. In this work, we propose SynFuzz, a novel approach to perform scalable concolic execution. SynFuzz achieves this goal by replacing interpretation with dynamic taint analysis and program synthesis. In particular, to flip a conditional branch, SynFuzz first uses operation-aware taint analysis to record a partial expression (i.e., a sketch) of its branch predicate. Then it uses oracle-guided program synthesis to reconstruct the symbolic expression based on input-output pairs. The last step is the same as traditional concolic execution - SynFuzz consults a SMT solver to generate an input that can flip the target branch. By doing so, SynFuzz can achieve an execution speed that is close to fuzzing while retain concolic execution's capability of flipping complex branch predicates. We have implemented a prototype of SynFuzz and evaluated it with three sets of programs: real-world applications, the LAVA-M benchmark, and the Google Fuzzer Test Suite (FTS). The evaluation results showed that SynFuzz was much more scalable than traditional concolic execution engines, was able to find more bugs in LAVA-M than most state-of-the-art concolic execution engine (QSYM), and achieved better code coverage on real-world applications and FTS.

Includes members:
  Member "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "evaluating fuzz testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "angora efficient fuzzing by principled search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "program synthesis using conflict driven learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings")
  Member "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hotfuzz discovering algorithmic denial of service vulnerabilities through guided micro fuzzing")
  Member "not all bytes are equal neural byte sieve for fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "learn fuzz machine learning for input fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "deepcoder learning to write programs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automl zero evolving machine learning algorithms from scratch")

Question: Please tell me which class the source paper 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)