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: "enfuzz from ensemble learning to ensemble fuzzing"
  Abstract: Fuzzing is widely used for software vulnerability detection. There are various kinds of fuzzers with different fuzzing strategies, and most of them perform well on their targets. However, in industry practice and empirical study, the performance and generalization ability of those well-designed fuzzing strategies are challenged by the complexity and diversity of real-world applications. #R##N#In this paper, inspired by the idea of ensemble learning, we first propose an ensemble fuzzing approach EnFuzz, that integrates multiple fuzzing strategies to obtain better performance and generalization ability than that of any constituent fuzzer alone. First, we define the diversity of the base fuzzers and choose those most recent and well-designed fuzzers as base fuzzers. Then, EnFuzz ensembles those base fuzzers with seed synchronization and result integration mechanisms. For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications. The 24-hour experiment indicates that, with the same resources usage, these four base fuzzers perform variously on different applications, while EnFuzz shows better generalization ability and always outperforms others in terms of path coverage, branch coverage and crash discovery. Even compared with the best cases of AFL, AFLFast, AFLGo and FairFuzz, EnFuzz discovers 26.8%, 117%, 38.8% and 39.5% more unique crashes, executes 9.16%, 39.2%, 19.9% and 20.0% more paths and covers 5.96%, 12.0%, 21.4% and 11.1% more branches respectively.

Connected through hyperedge HE-1 (induced by source paper "gasfuzz generating high gas consumption inputs to avoid out of gas vulnerability"):
  with members:
    - "evmfuzz differential fuzz testing of ethereum virtual machine"
    - "the ico phenomenon and its relationships with ethereum smart contract environment"
    - "gastap a gas analyzer for smart contracts"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "vandal a scalable security analysis framework for smart contracts"
    - "securify practical security analysis of smart contracts"
    - "an adaptive gas cost mechanism for ethereum to defend against under priced dos attacks"

Connected through hyperedge HE-2 (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"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-3 (induced by source paper "the strengths and behavioral quirks of java bytecode decompilers"):
  with members:
    - "towards neural decompilation"
    - "enfuzz ensemble fuzzing with seed synchronization among diverse fuzzers"

Connected through hyperedge HE-4 (induced by source paper "dlfuzz differential fuzzing testing of deep learning systems"):
  with members:
    - "deepxplore automated whitebox testing of deep learning systems"
    - "feature guided black box safety testing of deep neural networks"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "end to end learning for self driving cars"
    - "safety verification of deep neural networks"
    - "deep residual learning for image recognition"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

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)