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: "data mining for detecting bitcoin ponzi schemes"
  Abstract: Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives.

Connected through hyperedge HE-1 (induced by source paper "an overview on smart contracts challenges advances and platforms"):
  with members:
    - "the art of the scam demystifying honeypots in ethereum smart contracts"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "characterizing cryptocurrency exchange scams"):
  with members:
    - "characterizing eosio blockchain"
    - "large scale analysis of pop up scam on typosquatting urls"
    - "a large scale empirical study on industrial fake apps"
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "rebooting research on detecting repackaged android apps literature review and benchmark"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "an analysis of attacks on blockchain consensus"

Connected through hyperedge HE-3 (induced by source paper "charting the landscape of online cryptocurrency manipulation"):
  with members:
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "polyglot distributed word representations for multilingual nlp"

Connected through hyperedge HE-4 (induced by source paper "the art of the scam demystifying honeypots in ethereum smart contracts"):
  with members:
    - "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"
    - "vandal a scalable security analysis framework for smart contracts"
    - "securify practical security analysis of smart contracts"

Connected through hyperedge HE-5 (induced by source paper "characterizing and detecting money laundering activities on the bitcoin network"):
  with members:
    - "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics"
    - "social signals in the ethereum trading network"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "anomaly detection in the bitcoin system a network perspective"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "investigating mmm ponzi scheme on bitcoin"):
  with members:
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "deanonymizing tor hidden service users through bitcoin transactions analysis"
    - "blocktag design and applications of a tagging system for blockchain analysis"
    - "when a small leak sinks a great ship deanonymizing tor hidden service users through bitcoin transactions analysis"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "blocksci design and applications of a blockchain analysis platform"

Connected through hyperedge HE-7 (induced by source paper "cascading machine learning to attack bitcoin anonymity"):
  with members:
    - "characterizing entities in the bitcoin blockchain"
    - "anomaly detection in bitcoin network using unsupervised learning methods"
    - "bitcoin transaction graph analysis"
    - "deanonymisation of clients in bitcoin p2p network"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

Connected through hyperedge HE-8 (induced by source paper "an evaluation of bitcoin address classification based on transaction history summarization"):
  with members:
    - "characterizing entities in the bitcoin blockchain"
    - "a survey on security and privacy issues of bitcoin"
    - "xgboost a scalable tree boosting system"
    - "smote synthetic minority over sampling technique"

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)