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 "when a small leak sinks a great ship deanonymizing tor hidden service users through bitcoin transactions analysis"):
  Abstract: With the rapid increase of threats on the Internet, people are continuously seeking privacy and anonymity. Services such as Bitcoin and Tor were introduced to provide anonymity for online transactions and Web browsing. Due to its pseudonymity model, Bitcoin lacks retroactive operational security, which means historical pieces of information could be used to identify a certain user. We investigate the feasibility of deanonymizing users of Tor hidden services who rely on Bitcoin as a payment method by exploiting public information leaked from online social networks, the Blockchain, and onion websites. This, for example, allows an adversary to link a user with @alice Twitter address to a Tor hidden service with private.onion address by finding at least one past transaction in the Blockchain that involves their publicly declared Bitcoin addresses. #R##N#To demonstrate the feasibility of this deanonymization attack, we carried out a real-world experiment simulating a passive, limited adversary. We crawled 1.5K hidden services and collected 88 unique Bitcoin addresses. We then crawled 5B tweets and 1M BitcoinTalk forum pages and collected 4.2K and 41K unique Bitcoin addresses, respectively. Each user address was associated with an online identity along with its public profile information. By analyzing the transactions in the Blockchain, we were able to link 125 unique users to 20 Tor hidden services, including sensitive ones, such as The Pirate Bay and Silk Road. We also analyzed two case studies in detail to demonstrate the implications of the resulting information leakage on user anonymity. In particular, we confirm that Bitcoin addresses should always be considered exploitable, as they can be used to deanonymize users retroactively. This is especially important for Tor hidden service users who actively seek and expect privacy and anonymity.

Includes members:
  Member "the unreasonable effectiveness of address clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anti money laundering in bitcoin experimenting with graph convolutional networks for financial forensics")
  Member "bitcoin transaction graph analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "characterizing eosio blockchain")
  Member "anonymous online purchases with exhaustive operational security":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deanonymizing tor hidden service users through bitcoin transactions analysis")
  Member "bitcoin over tor isn t a good idea":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the security reference architecture for blockchains towards a standardized model for studying vulnerabilities threats and defenses")
  Member "content and popularity analysis of tor hidden services":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "content based features to rank influential hidden services of the tor darknet")
  Member "traveling the silk road a measurement analysis of a large anonymous online marketplace":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "detecting mixing services via mining bitcoin transaction network with hybrid motifs")

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.CR(Cryptography and Security)