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 "gasfuzz generating high gas consumption inputs to avoid out of gas vulnerability"):
  Abstract: The out-of-gas error occurs when smart contract programs are provided with inputs that cause excessive gas consumption, and would be easily exploited to make the DoS attack. Multiple approaches have been proposed to estimate the gas limit of a function in smart contracts to avoid such error. However, under estimation often happens when the contract is complicated. In this work, we propose GasFuzz, which could automatically generate inputs that maximizes the gas cost and reduce the under estimation cases. GasFuzz is designed based on feedback-directed mutational fuzz testing. First, GasFuzz builds the gas weighted control flow graph (CFG) of functions in smart contracts. Then, GasFuzz develops gas consumption guided selection and mutation strategies to generate the input that maximize the gas consumption. For evaluation, we implement GasFuzz based on js-evm, a widely used ethereum virtual machine written in javascript, and conduct experiments on 736 real-world transactions recorded on Ethereum. 44.02\% of the transactions would have out-of-gas errors under the estimation results given by solc, means that the recorded real gas consumption for those recorded transactions is larger than the gas limit value estimated by solc. While GasFuzz could reduce the under estimation ratio to 13.86\%. Compared with other well-known feedback-directed fuzzing engines such as PerFuzz and SlowFuzz, GasFuzz can generate a same or higher gas estimation value on 97.8\% of the recorded transactions with less time, usually within 5 minutes. Furthermore, GasFuzz has exposed 25 previously unknown out-of-gas vulnerabilities in those widely-used smart contracts, 5 of which have been assigned unique CVE identifiers in the US National Vulnerability Database.

Includes members:
  Member "evmfuzz differential fuzz testing of ethereum virtual machine":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an overview on smart contracts challenges advances and platforms")
  Member "the ico phenomenon and its relationships with ethereum smart contract environment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who are the phishers phishing scam detection on ethereum via network embedding")
  Member "gastap a gas analyzer for smart contracts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "step on the gas a better approach for recommending the ethereum gas price")
  Member "contractfuzzer fuzzing smart contracts for vulnerability detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smart contract development from the perspective of developers topics and issues discussed on social media")
  Member "a semantic framework for the security analysis of ethereum smart contracts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ae gis shielding vulnerable smart contracts against attacks")
  Member "enfuzz from ensemble learning to ensemble fuzzing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evmfuzz differential fuzz testing of ethereum virtual machine")
  Member "vandal a scalable security analysis framework for smart contracts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an overview on smart contracts challenges advances and platforms")
  Member "securify practical security analysis of smart contracts":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an overview on smart contracts challenges advances and platforms")

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)