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: "measurement and analysis of the bitcoin networks a view from mining pools"
  Abstract: Mining pools, the main components of the Bitcoin network, dominate the computing resources and play essential roles in network security and performance aspects. Although many existing measurements of the Bitcoin network are available, little is known about the details of mining pool behaviors (e.g., empty blocks, mining revenue and transaction collection strategies) and their effects on the Bitcoin end users (e.g., transaction fees, transaction delay and transaction acceptance rate). This paper aims to fill this gap with a systematic study of mining pools. We traced over 1.56 hundred thousand blocks (including about 257 million historical transactions) from February 2016 to January 2019 and collected over 120.25 million unconfirmed transactions from March 2018 to January 2019. Then we conducted a board range of measurements on the pool evolutions, labeled transactions (blocks) as well as real-time network traffics, and discovered new interesting observations and features. Specifically, our measurements show the following. 1) A few mining pools entities continuously control most of the computing resources of the Bitcoin network. 2) Mining pools are caught in a prisoner's dilemma where mining pools compete to increase their computing resources even though the unit profit of the computing resource decreases. 3) Mining pools are stuck in a Malthusian trap where there is a stage at which the Bitcoin incentives are inadequate for feeding the exponential growth of the computing resources. 4) The market price and transaction fees are not sensitive to the event of halving block rewards. 5) The block interval of empty blocks is significantly lower than the block interval of non-empty blocks. 6) Feerate plays a dominating role in transaction collection strategy for the top mining pools. Our measurements and analysis help to understand and improve the Bitcoin network.

Connected through hyperedge HE-1 (induced by source paper "xblock eth extracting and exploring blockchain data from ethereum"):
  with members:
    - "blockchain for internet of things a survey"
    - "decentralization in bitcoin and ethereum networks"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "an adaptive gas cost mechanism for ethereum to defend against under priced dos attacks"
    - "dissecting ponzi schemes on ethereum identification analysis and impact"
    - "blocksci design and applications of a blockchain analysis platform"
    - "blockbench a framework for analyzing private blockchains"
    - "online human bot interactions detection estimation and characterization"

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)