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: "the art of the scam demystifying honeypots in ethereum smart contracts"
  Abstract: Modern blockchains, such as Ethereum, enable the execution of so-called smart contracts - programs that are executed across the decentralised blockchain network. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In the past few years, several smart contracts have been found to be vulnerable and thus exploited by attackers. However, a new trend towards a more proactive approach seems to be on the rise where attackers do not search for vulnerable contracts anymore. Instead, they try to lure their victims into traps by deploying vulnerable-looking contracts that contain hidden traps. This type of contracts is commonly referred to as honeypots. In this paper, we present the first systematic analysis of honeypots, by investigating their prevalence, behaviour and impact on the Ethereum blockchain. We develop a taxonomy of honeypot techniques and use this to build HONEYBADGER - a tool that employs symbolic execution and well defined heuristics to expose smart contract honeypots. We perform a large-scale analysis of more than 2 million smart contracts and show that our tool not only achieves high precision, but also high scalability. We identify 690 honeypots as well as 240 victims in the wild, with an accumulated profit of more than $90,000 for the honeypot creators. Our manual validation shows that 87% of the reported contracts are indeed honeypots.

Connected through hyperedge HE-1 (induced by source paper "an overview on smart contracts challenges advances and platforms"):
  with members:
    - "blockchain for internet of things a survey"
    - "evmfuzz differential fuzz testing of ethereum virtual machine"
    - "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 "empirical review of automated analysis tools on 47 587 ethereum smart contracts"):
  with members:
    - "a survey on ethereum systems security vulnerabilities attacks and defenses"
    - "slither a static analysis framework for smart contracts"
    - "smart contract vulnerabilities does anyone care"
    - "domain specific code smells in smart contracts"
    - "manticore a user friendly symbolic execution framework for binaries and smart contracts"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "who are the phishers phishing scam detection on ethereum via network embedding"):
  with members:
    - "the ico phenomenon and its relationships with ethereum smart contract environment"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "a survey on security and privacy issues of bitcoin"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "entity embedding based anomaly detection for heterogeneous categorical events"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-4 (induced by source paper "a data science approach for honeypot detection in ethereum"):
  with members:
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "towards safer smart contracts a sequence learning approach to detecting vulnerabilities"
    - "securify practical security analysis of smart contracts"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "xgboost a scalable tree boosting system"

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.CR(Cryptography and Security)