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: "soliditycheck quickly detecting smart contract problems through regular expressions"
  Abstract: As a blockchain platform that has developed vigorously in recent years, Ethereum is different from Bitcoin in that it introduces smart contracts into blockchain.Solidity is one of the most mature and widely used smart contract programming language,which is used to write smart contracts and deploy them on blockchain. However, once the data in the blockchain is written, it cannot be modified. Ethereum smart contract is stored in the block chain, which makes the smart contract can no longer repair the code problems such as re-entrancy vulnerabilities or integer overflow problems. Currently, there still lacks of an efficient and effective approach for detecting these problems in Solidity. In this paper, we first classify all the possible problems in Solidity, then propose a smart contract problem detection approach for Solidity, namely SolidityCheck. The approach uses regular expressions to define the characteristics of problematic statements and uses regular matching and program instrumentation to prevent or detect problems. Finally, a large number of experiments is performed to show that SolidityCheck is superior to existing approaches.

Connected through hyperedge HE-1 (induced by source paper "ae gis shielding vulnerable smart contracts against attacks"):
  with members:
    - "slither a static analysis framework for smart contracts"
    - "harvey a greybox fuzzer for smart contracts"
    - "exploiting the laws of order in smart contracts"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "towards safer smart contracts a sequence learning approach to detecting security threats"
    - "towards safer smart contracts a sequence learning approach to detecting vulnerabilities"

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)