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: "evmfuzz differential fuzz testing of ethereum virtual machine"
  Abstract: Ethereum Virtual Machine (EVM) is the run-time environment for smart contracts and its vulnerabilities may lead to serious problems to the Ethereum ecology. With lots of techniques being developed for the validation of smart contracts, the security problems of EVM have not been well-studied. In this paper, we propose EVMFuzz, aiming to detect vulnerabilities of EVMs with differential fuzz testing. The core idea of EVMFuzz is to continuously generate seed contracts for different EVMs' execution, so as to find as many inconsistencies among execution results as possible, eventually discover vulnerabilities with output cross-referencing. First, we present the evaluation metric for the internal inconsistency indicator, such as the opcode sequence executed and gas used. Then, we construct seed contracts via a set of predefined mutators and employ dynamic priority scheduling algorithm to guide seed contracts selection and maximize the inconsistency. Finally, we leverage different EVMs as crossreferencing oracles to avoid manual checking of the execution output. For evaluation, we conducted large-scale mutation on 36,295 real-world smart contracts and generated 253,153 smart contracts. Among them, 66.2% showed differential performance, including 1,596 variant contracts triggered inconsistent output among EVMs. Accompanied by manual root cause analysis, we found 5 previously unknown security bugs in four widely used EVMs, and all had been included in Common Vulnerabilities and Exposures (CVE) database.

Connected through hyperedge HE-1 (induced by source paper "an overview on smart contracts challenges advances and platforms"):
  with members:
    - "the art of the scam demystifying honeypots in ethereum smart contracts"
    - "blockchain for internet of things a survey"
    - "tool demonstration fsolidm for designing secure ethereum smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "ethir a framework for high level analysis of ethereum bytecode"
    - "sabre protecting bitcoin against routing attacks"
    - "hunting the ethereum smart contract color inspired inspection of potential attacks"
    - "a survey on the security of blockchain systems"

Connected through hyperedge HE-2 (induced by source paper "mutation testing of smart contracts at scale"):
  with members:
    - "truffle tests for free replaying ethereum smart contracts for transparency"
    - "basis path coverage criteria for smart contract application testing"
    - "sif a framework for solidity code instrumentation and analysis"
    - "testing smart contracts gets smarter"
    - "oracle supported dynamic exploit generation for smart contracts"
    - "mutation testing for ethereum smart contract"
    - "user experience with language independent formal verification"

Connected through hyperedge HE-3 (induced by source paper "gasfuzz generating high gas consumption inputs to avoid out of gas vulnerability"):
  with members:
    - "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"
    - "enfuzz from ensemble learning to ensemble fuzzing"
    - "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"

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)