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: "tracking bitcoin users activity using community detection on a network of weak signals"
  Abstract: Bitcoin is a cryptocurrency attracting a lot of interest both from the general public and researchers. There is an ongoing debate on the question of users’ anonymity: while the Bitcoin protocol has been designed to ensure that the activity of individual users could not be tracked, some methods have been proposed to partially bypass this limitation. In this article, we show how the Bitcoin transaction network can be studied using complex networks analysis techniques, and in particular how community detection can be efficiently used to re-identify multiple addresses belonging to a same user.

Connected through hyperedge HE-1 (induced by source paper "blockchain for internet of things a survey"):
  with members:
    - "an overview on smart contracts challenges advances and platforms"
    - "repchain a reputation based secure fast and high incentive blockchain system via sharding"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "big data analytics for large scale wireless networks challenges and opportunities"
    - "big data analytics for manufacturing internet of things opportunities challenges and enabling technologies"
    - "analysis of the xrp ledger consensus protocol"
    - "sabre protecting bitcoin against routing attacks"
    - "how to make a digital currency on a blockchain stable"

Connected through hyperedge HE-2 (induced by source paper "do you trade with your friends or become friends with your trading partners a case study in the g1 cryptocurrency"):
  with members:
    - "weighted bipartite or directed stream graphs for the modeling of temporal networks"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"

Connected through hyperedge HE-3 (induced by source paper "flow motifs in interaction networks"):
  with members:
    - "motifs in temporal 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)