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 "who are the phishers phishing scam detection on ethereum via network embedding"):
  Abstract: Recently, blockchain technology has become a topic in the spotlight but also a hotbed of various cybercrimes. Among them, phishing scams on blockchain have been found making a notable amount of money, thus emerging as a serious threat to the trading security of the blockchain ecosystem. In order to create a favorable environment for investment, an effective method for detecting phishing scams is urgently needed in the blockchain ecosystem. To this end, this paper proposes an approach to detect phishing scams on Ethereum by mining its transaction records. Specifically, we first crawl the labeled phishing addresses from two authorized websites and reconstruct the transaction network according to the collected transaction records. Then, by taking the transaction amount and timestamp into consideration, we propose a novel network embedding algorithm called trans2vec to extract the features of the addresses for subsequent phishing identification. Finally, we adopt the oneclass support vector machine (SVM) to classify the nodes into normal and phishing ones. Experimental results demonstrate that the phishing detection method works effectively on Ethereum, and indicate the efficacy of trans2vec over existing state-of-the-art algorithms on feature extraction for transaction networks. This work is the first investigation on phishing detection on Ethereum via network embedding and provides insights into how features of large-scale transaction networks can be embedded.

Includes members:
  Member "the art of the scam demystifying honeypots in ethereum smart contracts":
    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 "gasfuzz generating high gas consumption inputs to avoid out of gas vulnerability")
  Member "a semantic framework for the security analysis of ethereum smart contracts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ae gis shielding vulnerable smart contracts against attacks")
  Member "a survey on security and privacy issues of bitcoin":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the feasibility of sybil attacks in shard based permissionless blockchains")
  Member "dissecting ponzi schemes on ethereum identification analysis and impact":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an overview on smart contracts challenges advances and platforms")
  Member "entity embedding based anomaly detection for heterogeneous categorical events":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supertml two dimensional word embedding for the precognition on structured tabular data")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep graph matching consensus")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "memory based graph networks")

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.SI(Social and Information Networks)