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: "ransomware payments in the bitcoin ecosystem"
  Abstract: Ransomware can prevent a user from accessing a device and its files until a ransom is paid to the attacker, most frequently in Bitcoin. With over 500 known ransomware families, it has become one of the dominant cybercrime threats for law enforcement, security professionals and the public. However, a more comprehensive, evidence-based picture on the global direct financial impact of ransomware attacks is still missing. In this paper, we present a data-driven method for identifying and gathering information on Bitcoin transactions related to illicit activity based on footprints left on the public Bitcoin blockchain. We implement this method on-top-of the GraphSense open-source platform and apply it to empirically analyze transactions related to 35 ransomware families. We estimate the lower bound direct financial impact of each ransomware family and find that, from 2013 to mid-2017, the market for ransomware payments has a minimum worth of USD 12,768,536 (22,967.54 BTC). We also find that the market is highly skewed with only a few number of players responsible for the majority of the payments. Based on these research findings, policy-makers and law enforcement agencies can use the statistics provided to understand the size of the illicit market and make informed decisions on how best to address the threat.

Connected through hyperedge HE-1 (induced by source paper "a prevention and a traction system for ransomware attacks"):
  with members:
    - "bitcoin transaction graph analysis"

Connected through hyperedge HE-2 (induced by source paper "safeguarding the evidential value of forensic cryptocurrency investigations"):
  with members:
    - "an empirical analysis of anonymity in zcash"
    - "an empirical analysis of linkability in the monero blockchain"
    - "blocksci design and applications of a blockchain analysis platform"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "when the cookie meets the blockchain privacy risks of web payments via cryptocurrencies"
    - "the unreasonable effectiveness of address clustering"

Connected through hyperedge HE-3 (induced by source paper "spams meet cryptocurrencies sextortion in the bitcoin ecosystem"):
  with members:
    - "blocksci design and applications of a blockchain analysis platform"
    - "economic analysis of ransomware"
    - "the unreasonable effectiveness of address clustering"
    - "learning to filter spam e mail a comparison of a naive bayesian and a memory based approach"

Connected through hyperedge HE-4 (induced by source paper "bitcoinheist topological data analysis for ransomware detection on the bitcoin blockchain"):
  with members:
    - "obfuscation in bitcoin techniques and politics"
    - "xgboost a scalable tree boosting system"
    - "bitcoin transaction graph analysis"

Connected through hyperedge HE-5 (induced by source paper "characterizing entities in the bitcoin blockchain"):
  with members:
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "toward open data blockchain analytics a bitcoin perspective"
    - "system g distributed graph database"
    - "when a small leak sinks a great ship deanonymizing tor hidden service users through bitcoin transactions analysis"
    - "blockchain a graph primer"
    - "the unreasonable effectiveness of address clustering"
    - "link prediction by de anonymization how we won the kaggle social network challenge"

Connected through hyperedge HE-6 (induced by source paper "a probabilistic model of the bitcoin blockchain"):
  with members:
    - "characterizing entities in the bitcoin blockchain"
    - "data mining for detecting bitcoin ponzi schemes"
    - "toward open data blockchain analytics a bitcoin perspective"
    - "blockchain a graph primer"
    - "the unreasonable effectiveness of address clustering"
    - "topic models conditioned on arbitrary features with dirichlet multinomial regression"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-7 (induced by source paper "scalable graph learning for anti money laundering a first look"):
  with members:
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"

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)