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 "checking smart contracts with structural code embedding"):
  Abstract: Smart contracts have been increasingly used together with blockchains to automate financial and business transactions. However, many bugs and vulnerabilities have been identified in many contracts which raises serious concerns about smart contract security, not to mention that the blockchain systems on which the smart contracts are built can be buggy. Thus, there is a significant need to better maintain smart contract code and ensure its high reliability. In this paper, we propose an automated approach to learn characteristics of smart contracts in Solidity, which is useful for clone detection, bug detection and contract validation on smart contracts. Our new approach is based on word embeddings and vector space comparison. We parse smart contract code into word streams with code structural information, convert code elements (e.g., statements, functions) into numerical vectors that are supposed to encode the code syntax and semantics, and compare the similarities among the vectors encoding code and known bugs, to identify potential issues. We have implemented the approach in a prototype, named SmartEmbed. Results show that our tool can effectively identify many repetitive instances of Solidity code, where the clone ratio is around 90\%. Code clones such as type-III or even type-IV semantic clones can also be detected accurately. Our tool can identify more than 1000 clone related bugs based on our bug databases efficiently and accurately. Our tool can also help to efficiently validate any given smart contract against a known set of bugs, which can help to improve the users' confidence in the reliability of the contract. #R##N#The anonymous replication packages can be accessed at: this https URL, and evaluated it with more than 22,000 smart contracts collected from the Ethereum blockchain.

Includes members:
  Member "automating app review response generation":
    also connected through other hyperedges:
      (none)
  Member "smartembed a tool for clone and bug detection in smart contracts through structural code embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "defining smart contract defects on ethereum")
  Member "sorting and transforming program repair ingredients via deep learning code similarities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a recurrent neural network based patch recommender for linux kernel bugs")
  Member "a general path based representation for predicting program properties":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structural language models of code")
  Member "a survey on the security of blockchain systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an overview on smart contracts challenges advances and platforms")
  Member "blockchain based smart contracts a systematic mapping study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an overview on smart contracts challenges advances and platforms")
  Member "under optimized smart contracts devour your money":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an overview on smart contracts challenges advances and platforms")
  Member "an empirical analysis of smart contracts platforms applications and design patterns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")

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.SE(Software Engineering)