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 "safeguarding the evidential value of forensic cryptocurrency investigations"):
  Abstract: Analyzing cryptocurrency payment flows has become a key forensic method in law enforcement and is nowadays used to investigate a wide spectrum of criminal activities. However, despite its widespread adoption, the evidential value of obtained findings in court is still largely unclear. In this paper, we focus on the key ingredients of modern cryptocurrency analytic techniques, which are clustering heuristics and attribution tags. By empirically quantifying the effect of CoinJoin transactions, we illustrate that clustering heuristics can lead to false interpretations. We then discuss clustering heuristics and attribution tags in the light of internationally accepted legal standards and rules for substantiating suspicions and providing evidence in court. From that we derive a set of legal key requirements and translate them into a data sharing framework that builds on existing legal and technical standards. Integrating that framework in modern cryptocurrency analytics tools could allow more efficient and effective investigations, while safeguarding their evidential value.

Includes members:
  Member "ransomware payments in the bitcoin ecosystem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a prevention and a traction system for ransomware attacks")
  Member "an empirical analysis of anonymity in zcash":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stake shift in major cryptocurrencies an empirical study")
  Member "an empirical analysis of linkability in the monero blockchain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "simulated blockchains for machine learning traceability and transaction values in the monero network")
  Member "blocksci design and applications of a blockchain analysis platform":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "xblock eth extracting and exploring blockchain data from ethereum")
  Member "counterfactual explanations without opening the black box automated decisions and the gdpr":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "when the cookie meets the blockchain privacy risks of web payments via cryptocurrencies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sok blockchain technology and its potential use cases")
  Member "the unreasonable effectiveness of address clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics")

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.CY(Computers and Society)