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: "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.

Connected through hyperedge HE-1 (induced by source paper "investigating mmm ponzi scheme on bitcoin"):
  with members:
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "data mining for detecting bitcoin ponzi schemes"
    - "deanonymizing tor hidden service users through bitcoin transactions analysis"
    - "blocktag design and applications of a tagging system for blockchain analysis"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "blocksci design and applications of a blockchain analysis platform"

Connected through hyperedge HE-2 (induced by source paper "characterizing entities in the bitcoin blockchain"):
  with members:
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "ransomware payments in the bitcoin ecosystem"
    - "toward open data blockchain analytics a bitcoin perspective"
    - "system g distributed graph database"
    - "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-3 (induced by source paper "can cryptocurrencies preserve privacy and comply with regulations"):
  with members:
    - "an empirical analysis of anonymity in zcash"
    - "when the cookie meets the blockchain privacy risks of web payments via cryptocurrencies"

Connected through hyperedge HE-4 (induced by source paper "tandem securing keys by using a central server while preserving privacy"):
  with members:
    - "when the cookie meets the blockchain privacy risks of web payments via cryptocurrencies"

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)